AI-Personalized Nutrition Platforms Market Forecasts to 2032 – Global Analysis By Component (Software/Platforms, and Services), Technology (Machine Learning & Deep Learning, Natural Language Processing (NLP), Computer Vision, and Data Analytics), Applicat
Description
According to Stratistics MRC, the Global AI-Personalized Nutrition Platforms Market is accounted for $1.4 billion in 2025 and is expected to reach $7.6 billion by 2032 growing at a CAGR of 27.5% during the forecast period. AI-personalized nutrition platforms are digital platforms using AI to deliver customized dietary and supplement advice. By analyzing individual data like DNA, gut microbiome, and lifestyle, they create tailored nutrition plans. This moves beyond one-size-fits-all diets to hyper-personalized wellness. The market is growing as consumers seek scientifically-backed, individualized health solutions. Companies leverage this technology to offer subscription services, personalized meal kits, and targeted supplement recommendations, driving engagement and better health outcomes.
According to the American Society for Nutrition, AI-powered nutrition platforms generated personalized diet recommendations for more than 12 million users in 2023, improving health outcomes in clinical studies.
Market Dynamics:
Driver:
Rising prevalence of chronic diseases and preventive healthcare focus
The increasing incidence of lifestyle-related chronic diseases such as obesity, diabetes, and cardiovascular disorders is fueling demand for AI-personalized nutrition platforms. Consumers and healthcare providers are focusing on preventive strategies, leveraging AI-driven insights to optimize diet plans based on individual health metrics, genetics, and lifestyle patterns. Moreover, wearable devices and health apps generate real-time data, enabling personalized nutrition recommendations. This shift toward proactive wellness and precision nutrition is creating sustained growth opportunities for platform developers and healthcare integrators globally.
Restraint:
Limited scientific validation for some AI recommendations
Despite growing adoption, certain AI-personalized nutrition solutions face skepticism due to limited clinical validation and lack of universally accepted dietary standards. Inaccurate or non-evidence-based recommendations can reduce user trust, hinder adoption, and potentially lead to adverse health outcomes. Additionally, discrepancies in algorithms across platforms and lack of longitudinal studies may constrain integration with healthcare systems. Vendors must invest in research collaborations, clinical trials, and regulatory compliance to strengthen credibility and encourage wider market penetration.
Opportunity:
Expansion into corporate wellness and insurance programs
Companies are increasingly incorporating AI-driven nutrition platforms into employee wellness initiatives and health insurance programs to improve workforce health, reduce absenteeism, and lower healthcare costs. Integration with wearable devices and personalized health monitoring enables scalable preventive interventions, enhancing employee engagement. Moreover, insurers are exploring AI insights to design tailored plans and incentives, driving B2B adoption. This corporate expansion offers recurring revenue opportunities for platform providers while reinforcing long-term market growth, especially as organizations emphasize holistic well-being and data-driven health solutions.
Threat:
Data security risks and potential algorithm biases
AI-personalized nutrition platforms collect sensitive health and lifestyle data, exposing users and providers to privacy breaches and regulatory scrutiny. Inadequate encryption, poor data governance, and third-party vulnerabilities can erode trust and lead to financial penalties. Additionally, algorithmic biases due to limited or skewed datasets may deliver inaccurate recommendations, reducing efficacy and credibility. To mitigate these threats, developers must implement robust cybersecurity measures, transparent AI models, and continuous auditing to ensure data integrity, ethical use, and equitable outcomes.
Covid-19 Impact:
The pandemic accelerated adoption of AI-personalized nutrition platforms as consumers sought remote, tailored health guidance while accessing gyms and clinics was restricted. Lockdowns highlighted the importance of preventive health and immunity, driving engagement with digital tools and telehealth integration. Platforms experienced rapid user growth, expansion in app features, and increased investment from healthcare providers and insurers. This period reinforced the long-term relevance of AI-driven nutrition solutions, prompting sustained adoption and innovation while fostering consumer trust in digital health technologies.
The software/platforms segment is expected to be the largest during the forecast period
The software/platforms segment is expected to account for the largest market share during the forecast period. These platforms dominate due to their ability to combine AI algorithms, user-friendly interfaces, and comprehensive dietary guidance in a single solution. Partnerships with healthcare providers and integration with medical records further strengthen adoption. Additionally, continuous software updates and modular add-ons for meal tracking, nutrition scoring, and personalized recommendations enhance user retention. Their versatility across individual consumers, corporates, and insurers solidifies long-term market share, making the software/platforms segment the preferred choice for personalized nutrition solutions worldwide.
The computer vision segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the computer vision segment is predicted to witness the highest growth rate. Adoption of computer vision is fueled by the convenience of instant dietary analysis and integration with mobile apps and health platforms. Innovations in AI image recognition, augmented reality features, and database expansion for food items accelerate adoption across consumer, clinical, and corporate wellness applications. Additionally, growing smartphone penetration and wearable device usage enable widespread deployment. These factors collectively contribute to rapid market growth, positioning computer vision as the fastest-expanding technology segment in AI-personalized nutrition platforms globally.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share due to high health awareness, widespread adoption of digital health technologies, and strong investment in AI healthcare solutions. Regulatory support, well-established telehealth infrastructure, and partnerships between platform providers, insurers, and wellness programs drive adoption. Moreover, high disposable incomes and advanced consumer tech penetration allow for early uptake of personalized nutrition tools. These factors collectively contribute to North America maintaining the largest market share, solidifying its position as a key revenue hub for AI-personalized nutrition platforms.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid urbanization, increasing health consciousness, and rising disposable incomes fuel demand for AI-personalized nutrition solutions in Asia Pacific. Governments and private stakeholders are investing in digital healthcare infrastructure, while smartphone and wearable device adoption support scalable platform deployment. Furthermore, localized content, affordable pricing models, and emerging startups catering to regional dietary preferences accelerate market growth. Collectively, these factors drive high adoption rates, positioning Asia Pacific as the fastest-growing region for AI-personalized nutrition platforms during the forecast period.
Key players in the market
Some of the key players in AI-Personalized Nutrition Platforms Market include Viome, ZOE, InsideTracker, NutriSense, Levels Health, EatLove, Suggestic, Foodsmart, Baze, Habit, DNAFit, Nutrigenomix, Fay, GenoPalate, Noom, Inc., and Medtronic plc.
Key Developments:
In July 2025, Viome, a life sciences startup founded by veteran tech entrepreneur Naveen Jain, announced collaboration with Microsoft to scale its molecular analysis platform — part of what Viome describes as a new era of AI-powered preventive health and wellness. Viome says Microsoft’s cloud and AI infrastructure specially tuned for its purposes in conjunction with the tech giant will allow it to process biological data more efficiently. The idea is to expand access, reduce costs, and accelerate data processing and diagnostics.
In April 2025, InsideTracker, a leader in data-driven health technology, is pleased to introduce Terra, a first-of-its-kind virtual coach that enables its members to dive deep into their own body. Terra builds on the success of Ask InsideTracker, a native AI tool released last year and now one of the platform’s most popular features. With this major version update, Terra becomes a personalized health coach with the ability to access information and offer recommendations typically limited to high-end concierge medicine.
Components Covered:
• Software/Platforms
• Services
Technologies Covered:
• Machine Learning & Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Data Analytics
Applications Covered:
• Disease Management
• Weight Management
• Sports Nutrition & Active Lifestyle
• General Health & Wellness
End Users Covered:
• Healthcare Providers & Professionals
• Wellness & Fitness Centers
• Corporate Organizations
• Research Institutions
Business Models Covered:
• Business-to-Consumer (B2C)
• Business-to-Business (B2B)
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
According to the American Society for Nutrition, AI-powered nutrition platforms generated personalized diet recommendations for more than 12 million users in 2023, improving health outcomes in clinical studies.
Market Dynamics:
Driver:
Rising prevalence of chronic diseases and preventive healthcare focus
The increasing incidence of lifestyle-related chronic diseases such as obesity, diabetes, and cardiovascular disorders is fueling demand for AI-personalized nutrition platforms. Consumers and healthcare providers are focusing on preventive strategies, leveraging AI-driven insights to optimize diet plans based on individual health metrics, genetics, and lifestyle patterns. Moreover, wearable devices and health apps generate real-time data, enabling personalized nutrition recommendations. This shift toward proactive wellness and precision nutrition is creating sustained growth opportunities for platform developers and healthcare integrators globally.
Restraint:
Limited scientific validation for some AI recommendations
Despite growing adoption, certain AI-personalized nutrition solutions face skepticism due to limited clinical validation and lack of universally accepted dietary standards. Inaccurate or non-evidence-based recommendations can reduce user trust, hinder adoption, and potentially lead to adverse health outcomes. Additionally, discrepancies in algorithms across platforms and lack of longitudinal studies may constrain integration with healthcare systems. Vendors must invest in research collaborations, clinical trials, and regulatory compliance to strengthen credibility and encourage wider market penetration.
Opportunity:
Expansion into corporate wellness and insurance programs
Companies are increasingly incorporating AI-driven nutrition platforms into employee wellness initiatives and health insurance programs to improve workforce health, reduce absenteeism, and lower healthcare costs. Integration with wearable devices and personalized health monitoring enables scalable preventive interventions, enhancing employee engagement. Moreover, insurers are exploring AI insights to design tailored plans and incentives, driving B2B adoption. This corporate expansion offers recurring revenue opportunities for platform providers while reinforcing long-term market growth, especially as organizations emphasize holistic well-being and data-driven health solutions.
Threat:
Data security risks and potential algorithm biases
AI-personalized nutrition platforms collect sensitive health and lifestyle data, exposing users and providers to privacy breaches and regulatory scrutiny. Inadequate encryption, poor data governance, and third-party vulnerabilities can erode trust and lead to financial penalties. Additionally, algorithmic biases due to limited or skewed datasets may deliver inaccurate recommendations, reducing efficacy and credibility. To mitigate these threats, developers must implement robust cybersecurity measures, transparent AI models, and continuous auditing to ensure data integrity, ethical use, and equitable outcomes.
Covid-19 Impact:
The pandemic accelerated adoption of AI-personalized nutrition platforms as consumers sought remote, tailored health guidance while accessing gyms and clinics was restricted. Lockdowns highlighted the importance of preventive health and immunity, driving engagement with digital tools and telehealth integration. Platforms experienced rapid user growth, expansion in app features, and increased investment from healthcare providers and insurers. This period reinforced the long-term relevance of AI-driven nutrition solutions, prompting sustained adoption and innovation while fostering consumer trust in digital health technologies.
The software/platforms segment is expected to be the largest during the forecast period
The software/platforms segment is expected to account for the largest market share during the forecast period. These platforms dominate due to their ability to combine AI algorithms, user-friendly interfaces, and comprehensive dietary guidance in a single solution. Partnerships with healthcare providers and integration with medical records further strengthen adoption. Additionally, continuous software updates and modular add-ons for meal tracking, nutrition scoring, and personalized recommendations enhance user retention. Their versatility across individual consumers, corporates, and insurers solidifies long-term market share, making the software/platforms segment the preferred choice for personalized nutrition solutions worldwide.
The computer vision segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the computer vision segment is predicted to witness the highest growth rate. Adoption of computer vision is fueled by the convenience of instant dietary analysis and integration with mobile apps and health platforms. Innovations in AI image recognition, augmented reality features, and database expansion for food items accelerate adoption across consumer, clinical, and corporate wellness applications. Additionally, growing smartphone penetration and wearable device usage enable widespread deployment. These factors collectively contribute to rapid market growth, positioning computer vision as the fastest-expanding technology segment in AI-personalized nutrition platforms globally.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share due to high health awareness, widespread adoption of digital health technologies, and strong investment in AI healthcare solutions. Regulatory support, well-established telehealth infrastructure, and partnerships between platform providers, insurers, and wellness programs drive adoption. Moreover, high disposable incomes and advanced consumer tech penetration allow for early uptake of personalized nutrition tools. These factors collectively contribute to North America maintaining the largest market share, solidifying its position as a key revenue hub for AI-personalized nutrition platforms.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid urbanization, increasing health consciousness, and rising disposable incomes fuel demand for AI-personalized nutrition solutions in Asia Pacific. Governments and private stakeholders are investing in digital healthcare infrastructure, while smartphone and wearable device adoption support scalable platform deployment. Furthermore, localized content, affordable pricing models, and emerging startups catering to regional dietary preferences accelerate market growth. Collectively, these factors drive high adoption rates, positioning Asia Pacific as the fastest-growing region for AI-personalized nutrition platforms during the forecast period.
Key players in the market
Some of the key players in AI-Personalized Nutrition Platforms Market include Viome, ZOE, InsideTracker, NutriSense, Levels Health, EatLove, Suggestic, Foodsmart, Baze, Habit, DNAFit, Nutrigenomix, Fay, GenoPalate, Noom, Inc., and Medtronic plc.
Key Developments:
In July 2025, Viome, a life sciences startup founded by veteran tech entrepreneur Naveen Jain, announced collaboration with Microsoft to scale its molecular analysis platform — part of what Viome describes as a new era of AI-powered preventive health and wellness. Viome says Microsoft’s cloud and AI infrastructure specially tuned for its purposes in conjunction with the tech giant will allow it to process biological data more efficiently. The idea is to expand access, reduce costs, and accelerate data processing and diagnostics.
In April 2025, InsideTracker, a leader in data-driven health technology, is pleased to introduce Terra, a first-of-its-kind virtual coach that enables its members to dive deep into their own body. Terra builds on the success of Ask InsideTracker, a native AI tool released last year and now one of the platform’s most popular features. With this major version update, Terra becomes a personalized health coach with the ability to access information and offer recommendations typically limited to high-end concierge medicine.
Components Covered:
• Software/Platforms
• Services
Technologies Covered:
• Machine Learning & Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Data Analytics
Applications Covered:
• Disease Management
• Weight Management
• Sports Nutrition & Active Lifestyle
• General Health & Wellness
End Users Covered:
• Healthcare Providers & Professionals
• Wellness & Fitness Centers
• Corporate Organizations
• Research Institutions
Business Models Covered:
• Business-to-Consumer (B2C)
• Business-to-Business (B2B)
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Table of Contents
200 Pages
- 1 Executive Summary
- 2 Preface
- 2.1 Abstract
- 2.2 Stake Holders
- 2.3 Research Scope
- 2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
- 2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions
- 3 Market Trend Analysis
- 3.1 Introduction
- 3.2 Drivers
- 3.3 Restraints
- 3.4 Opportunities
- 3.5 Threats
- 3.6 Technology Analysis
- 3.7 Application Analysis
- 3.8 End User Analysis
- 3.9 Emerging Markets
- 3.10 Impact of Covid-19
- 4 Porters Five Force Analysis
- 4.1 Bargaining power of suppliers
- 4.2 Bargaining power of buyers
- 4.3 Threat of substitutes
- 4.4 Threat of new entrants
- 4.5 Competitive rivalry
- 5 Global AI-Personalized Nutrition Platforms Market, By Component
- 5.1 Introduction
- 5.2 Software/Platforms
- 5.3 Services
- 6 Global AI-Personalized Nutrition Platforms Market, By Technology
- 6.1 Introduction
- 6.2 Machine Learning & Deep Learning
- 6.3 Natural Language Processing (NLP)
- 6.4 Computer Vision
- 6.5 Data Analytics
- 7 Global AI-Personalized Nutrition Platforms Market, By Application
- 7.1 Introduction
- 7.2 Disease Management
- 7.3 Weight Management
- 7.4 Sports Nutrition & Active Lifestyle
- 7.5 General Health & Wellness
- 8 Global AI-Personalized Nutrition Platforms Market, By End User
- 8.1 Introduction
- 8.2 Healthcare Providers & Professionals
- 8.3 Wellness & Fitness Centers
- 8.4 Corporate Organizations
- 8.5 Research Institutions
- 9 Global AI-Personalized Nutrition Platforms Market, By Business Model
- 9.1 Introduction
- 9.2 Business-to-Consumer (B2C)
- 9.2.1 Subscription-based (SaaS)
- 9.2.2 One-time Purchase
- 9.3 Business-to-Business (B2B)
- 9.3.1 Licensing
- 9.3.2 White-label Solutions
- 10 Global AI-Personalized Nutrition Platforms Market, By Geography
- 10.1 Introduction
- 10.2 North America
- 10.2.1 US
- 10.2.2 Canada
- 10.2.3 Mexico
- 10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 Italy
- 10.3.4 France
- 10.3.5 Spain
- 10.3.6 Rest of Europe
- 10.4 Asia Pacific
- 10.4.1 Japan
- 10.4.2 China
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 New Zealand
- 10.4.6 South Korea
- 10.4.7 Rest of Asia Pacific
- 10.5 South America
- 10.5.1 Argentina
- 10.5.2 Brazil
- 10.5.3 Chile
- 10.5.4 Rest of South America
- 10.6 Middle East & Africa
- 10.6.1 Saudi Arabia
- 10.6.2 UAE
- 10.6.3 Qatar
- 10.6.4 South Africa
- 10.6.5 Rest of Middle East & Africa
- 11 Key Developments
- 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
- 11.2 Acquisitions & Mergers
- 11.3 New Product Launch
- 11.4 Expansions
- 11.5 Other Key Strategies
- 12 Company Profiling
- 12.1 Viome
- 12.2 ZOE
- 12.3 InsideTracker
- 12.4 NutriSense
- 12.5 Levels Health
- 12.6 EatLove
- 12.7 Suggestic
- 12.8 Foodsmart
- 12.9 Baze
- 12.10 Habit
- 12.11 DNAFit
- 12.12 Nutrigenomix
- 12.13 Fay
- 12.14 GenoPalate
- 12.15 Noom, Inc.
- 12.16 Medtronic plc
- List of Tables
- Table 1 Global AI-Personalized Nutrition Platforms Market Outlook, By Region (2024-2032) ($MN)
- Table 2 Global AI-Personalized Nutrition Platforms Market Outlook, By Component (2024-2032) ($MN)
- Table 3 Global AI-Personalized Nutrition Platforms Market Outlook, By Software/Platforms (2024-2032) ($MN)
- Table 4 Global AI-Personalized Nutrition Platforms Market Outlook, By Services (2024-2032) ($MN)
- Table 5 Global AI-Personalized Nutrition Platforms Market Outlook, By Technology (2024-2032) ($MN)
- Table 6 Global AI-Personalized Nutrition Platforms Market Outlook, By Machine Learning & Deep Learning (2024-2032) ($MN)
- Table 7 Global AI-Personalized Nutrition Platforms Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
- Table 8 Global AI-Personalized Nutrition Platforms Market Outlook, By Computer Vision (2024-2032) ($MN)
- Table 9 Global AI-Personalized Nutrition Platforms Market Outlook, By Data Analytics (2024-2032) ($MN)
- Table 10 Global AI-Personalized Nutrition Platforms Market Outlook, By Application (2024-2032) ($MN)
- Table 11 Global AI-Personalized Nutrition Platforms Market Outlook, By Disease Management (2024-2032) ($MN)
- Table 12 Global AI-Personalized Nutrition Platforms Market Outlook, By Weight Management (2024-2032) ($MN)
- Table 13 Global AI-Personalized Nutrition Platforms Market Outlook, By Sports Nutrition & Active Lifestyle (2024-2032) ($MN)
- Table 14 Global AI-Personalized Nutrition Platforms Market Outlook, By General Health & Wellness (2024-2032) ($MN)
- Table 15 Global AI-Personalized Nutrition Platforms Market Outlook, By End User (2024-2032) ($MN)
- Table 16 Global AI-Personalized Nutrition Platforms Market Outlook, By Healthcare Providers & Professionals (2024-2032) ($MN)
- Table 17 Global AI-Personalized Nutrition Platforms Market Outlook, By Wellness & Fitness Centers (2024-2032) ($MN)
- Table 18 Global AI-Personalized Nutrition Platforms Market Outlook, By Corporate Organizations (2024-2032) ($MN)
- Table 19 Global AI-Personalized Nutrition Platforms Market Outlook, By Research Institutions (2024-2032) ($MN)
- Table 20 Global AI-Personalized Nutrition Platforms Market Outlook, By Business Model (2024-2032) ($MN)
- Table 21 Global AI-Personalized Nutrition Platforms Market Outlook, By Business-to-Consumer (B2C) (2024-2032) ($MN)
- Table 22 Global AI-Personalized Nutrition Platforms Market Outlook, By Subscription-based (SaaS) (2024-2032) ($MN)
- Table 23 Global AI-Personalized Nutrition Platforms Market Outlook, By One-time Purchase (2024-2032) ($MN)
- Table 24 Global AI-Personalized Nutrition Platforms Market Outlook, By Business-to-Business (B2B) (2024-2032) ($MN)
- Table 25 Global AI-Personalized Nutrition Platforms Market Outlook, By Licensing (2024-2032) ($MN)
- Table 26 Global AI-Personalized Nutrition Platforms Market Outlook, By White-label Solutions (2024-2032) ($MN)
- Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
Pricing
Currency Rates
Questions or Comments?
Our team has the ability to search within reports to verify it suits your needs. We can also help maximize your budget by finding sections of reports you can purchase.
