AI in Nutrigenomics and Personalized Nutrition Market Size, Share and Forecast 2026 to 2035
Share With :
Global AI in Nutrigenomics and Personalized Nutrition Market Size is valued at US$ 3.55 Bn in 2025 and is predicted to reach US$ 16.19 Bn by the year 2035 at an 16.5% CAGR during the forecast period for 2026 to 2035.
AI in Nutrigenomics and Personalized Nutrition Market Size, Share & Trends Analysis Distribution By Type of Product (Dietary Supplements (Vitamins, Minerals, Probiotics, Prebiotics, Botanicals, Proteins, Carbohydrates, and Fats), Functional Foods, and Nutraceuticals), By Type of Service (Dietary Assessment, Nutrigenomics, Personalized Meal Planning, Lifestyle Assessment, and Health Monitoring), By Type of Technology (Machine Learning, Natural Language Processing, Computer Vision, and Predictive Analytics), by Type of Component (Software, Hardware, and Services), By Application Area (Weight Management, Sports Nutrition, Digestive Health, Cognitive Health, and Immune Health), By Type of Device, By Type of Deployment Mode , By Region and Segment Forecasts, 2026 to 2035.
AI in Nutrigenomics and Personalized Nutrition Market Key Takeaways:
|
AI in nutrigenomics and personalized nutrition refers to the use of artificial intelligence to analyze genetic, metabolic, and lifestyle data, enabling customized dietary recommendations that optimize individual health, prevent diseases, and enhance overall well-being.
The AI in Nutrigenomics and Personalized Nutrition market is rapidly expanding as artificial intelligence improves genetic data analysis to make accurate dietary recommendations. AI algorithms read genomic, metabolic, and lifestyle data to personalize nutrition plans that maximize individual health outcomes. The increased significance of nutrigenomics comes from the fact that it can connect genes with responses to nutrients, making truly personalized nutrition possible. One of the major drivers for this market is growing consumer interest in data-informed health solutions that avoid chronic conditions, promote wellness, and extend life. Also, advances in genomics, wearable health technology, and machine learning drive adoption across healthcare and nutrition markets.
The AI in nutrigenomics and personalized nutrition market is being driven by the rising adoption of medical wearable devices that track biomarkers such as glucose, heart rate, sleep patterns, and physical activity in real time. These devices develop vast datasets that, when integrated with AI algorithms, allow for highly personalized dietary and health recommendations. The growing consumer preference for data-driven wellness insights, combined with the increasing prevalence of lifestyle-related diseases, is fueling the demand for AI-powered nutrition platforms. Furthermore, healthcare professionals are leveraging wearable-generated data to design adaptive nutritional plans, improving preventive healthcare and personalized treatment outcomes.
Some of the Key Players in the AI in Nutrigenomics and Personalized Nutrition Market:
The AI in nutrigenomics and personalized nutrition market is segmented by type of product, type of services, type of technology, type of component, application area, type of device, type of deployment mode, and by region. By type of product, the market is segmented into dietary supplements (vitamins, minerals, probiotics, prebiotics, botanicals, proteins, carbohydrates, and fats), functional foods, and nutraceuticals. By type of services, the market is segmented into dietary assessment, nutrigenomics, personalized meal planning, lifestyle assessment, and health monitoring. By type of technology, the market is segmented into machine learning, natural language processing, computer vision, and predictive analytics. By type of component, the market is segmented into software, hardware, and services. By application area, the market is segmented into weight management, sports nutrition, digestive health, cognitive health, and immune health. By type of device, the market is segmented into wearables, smartphones, and tablets. By type of deployment type, the market is segmented into cloud-based, and on-premises.
In 2024, the dietary supplements held the major market share over the projected period due to rising adoption of dietary supplements tailored to individual genetic profiles. Artificial intelligence enables the analysis of genetic, metabolic, and lifestyle data to develop personalised nutrition plans, thereby optimising supplement efficacy and health outcomes. Growing consumer awareness of preventive medicine and chronic disease management is driving the demand for AI-based personalised nutrition. Further boosting market growth worldwide are growing applications of machine learning in diet assessment and predictive health analytics.
The AI in nutrigenomics and personalized nutrition market is dominated by software due to the growing demand for tailored health and wellness solutions. Increasing health consciousness, combined with rising incidences of lifestyle disorders such as obesity and diabetes, is driving the adoption of AI-based solutions that interpret genomic information for personalized nutrition recommendations. Further, advances in machine learning and bioinformatics, robust research infrastructure, and growing collaborations among biotech firms and healthcare professionals further drive market growth in the region.
North America dominates the market for AI in nutrigenomics and personalized nutrition due to region’s rising demand for customized dietary plans based on genetic profiles. Growing health awareness, coupled with increasing prevalence of lifestyle-related disorders such as obesity and diabetes, is fueling the adoption of AI-driven solutions that analyse genomic data for personalised nutrition insights. Additionally, advancements in machine learning and bioinformatics, strong research infrastructure, and expanding partnerships between biotech firms and healthcare providers further accelerate market growth in the region.
Moreover, Europe's AI in Nutrigenomics and Personalised Nutrition market is also fueled due to the region’s increasing consumer awareness of personalised health solutions and preventive care. The rising prevalence of lifestyle-related diseases such as obesity, diabetes, and cardiovascular disorders drives demand for tailored nutrition plans. Advances in AI, genomics, and bioinformatics enable the delivery of precise diet recommendations tailored to individual genetic profiles. Additionally, declining costs of genetic testing, supportive government initiatives, and growing investment in health technology startups are accelerating market growth across Europe.
| Report Attribute | Specifications |
| Market Size Value In 2025 | US$ 3.55 Bn |
| Revenue Forecast In 2035 | US$ 16.19 Bn |
| Growth Rate CAGR | CAGR of 16.5% from 2026 to 2035 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2026 to 2035 |
| Historic Year | 2022 to 2025 |
| Forecast Year | 2026-2035 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type of Product, By Type of Service, By Type of Technology, By Type of Component, By Application Area, By Type of Device, By Type of Deployment Mode |
| Regional Scope | North America, Europe, Asia Pacific, Latin America, Middle East & Africa |
| Country Scope | U.S., Canada, Germany, The UK, France, Italy, Spain, Rest of Europe, China, Japan, India, South Korea, Southeast Asia, Rest of Asia Pacific, Brazil, Argentina, Mexico, Rest of Latin America, GCC Countries, South Africa, Rest of the Middle East and Africa |
| Competitive Landscape | Appinventiv, BetterMeal AI, Culina Health, DayTwo, EatLove, LemonBox, Nutrify, Nourished, Nutrino Health, Nutrigenomix, Persona Nutrition, Viome, and Zoe |
| Customization Scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Geographic competitive landscape. |
| Pricing and Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in Nutrigenomics and Personalized Nutrition Market, by Type of Component-
AI in Nutrigenomics and Personalized Nutrition Market by Type of Device-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.
See the data, methodology, and competitive landscape preview — delivered to your inbox shortly.
Trusted by Sartorius, L'Oreal, Fujifilm & 370+ organizations