AI in Personalized Nutrition Market Size was valued at USD 4.13 Bn in 2024 and is predicted to reach USD 20.98 Bn by 2034 at a 17.8% CAGR during the forecast period for 2025-2034.
AI in personalized nutrition has emerged as a transformative force across various industries, revolutionizing how we perceive and utilize data. In nutrition, Al plays a pivotal role in shaping personalized approaches to cater to individual dietary needs, health goals, and genetic predispositions. One of the core elements propelling the growth of Al in personalized nutrition is its ability to leverage sophisticated segmentation strategies. Traditionally, nutrition recommendations were based on generalized guidelines, often overlooking individuals' unique physiological and genetic makeup. Al-driven segmentation now enables a granular understanding of each person's requirements, encompassing age, gender, lifestyle, medical history, allergies, and genetic data.
The integration of Al in personalized nutrition has witnessed exponential growth in recent years. With the escalating prevalence of chronic diseases, obesity, and lifestyle disorders, consumers are increasingly seeking tailored dietary solutions. The Al-driven personalized nutrition market has responded to this demand, offering easy-to-use mobile applications, wearable devices, and online platforms that provide users with personalized dietary insights.
AI in the personalized nutrition market is segmented by type, application, end-user, and provider. The market is segmented based on type as machine learning, deep learning, natural language processing (NLP), and computer vision. The market is segmented based on application into meal planning and recommendations, nutrient analysis, personalized supplementation, allergen and sensitivity detection, and health monitoring. Based on end-user, the market is segmented into individuals, fitness and wellness centers, healthcare providers, and food manufacturers. The market is segmented based on provider: startups and small companies, established tech companies, and healthcare and wellness organizations.
The machine learning segment is expected to hold a major share in the global AI in personalized nutrition market in 2023. To deliver tailored nutritional recommendations, machine learning algorithms analyze vast datasets, including dietary habits, genetic information, and health metrics. These algorithms continuously improve their accuracy by learning from new data and user feedback. As a result, they provide highly individualized diet plans that optimize health outcomes. The increasing adoption of wearable devices and health apps further drives this segment, as they supply valuable data that Machine Learning models use to refine their recommendations, enhancing their effectiveness in personalized nutrition.
AI systems analyze individual health data, dietary preferences, and genetic information to create customized meal plans that optimize nutritional intake and health outcomes. These solutions use algorithms to offer real-time dietary suggestions, adjust recommendations based on user feedback, and integrate with wearable devices for continuous monitoring. The market's growth is driven by increasing health consciousness, technological advancements, and rising demand for personalized wellness solutions. North America leads in market share, while Asia Pacific is experiencing rapid growth due to increasing health awareness and technology adoption.
The North American AI in the personalized nutrition market is expected to register the highest market share in terms of revenue in the near future. The region's advanced technological infrastructure, high healthcare expenditure, and increasing consumer demand for tailored nutrition solutions. The presence of major tech companies and startups focused on AI-driven health innovations further bolsters market growth. The region's strong emphasis on research and development, coupled with favorable regulatory environments, also supports the expansion of personalized nutrition solutions. Additionally, the rising awareness of health and wellness trends among North American consumers fuels the adoption of AI-powered dietary recommendations and personalized nutrition plans.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 4.13 Bn |
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Revenue Forecast In 2034 |
USD 20.98 Bn |
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Growth Rate CAGR |
CAGR of 17.8% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application, End-user, Provider |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Nutrino Health Ltd., DayTwo Ltd., Lumen, Nutrigenomix Inc., Viome, Foodvisor, Baze Labs, GenoPalate, Habit, Nutraceutical Corporation, Nutrafol, Zoe, Healbe, FitGenie, Level Foods Inc., Nutrunity, NutriAI, NutrinoTech, AIMEE Health, NutriMe, PreBiomics, NutrEval, NutriAdmin, Nutrissential, NutriPredict, and others |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Artificial Intelligence in Personalized Nutrition Market Snapshot
Chapter 4. Global Artificial Intelligence in Personalized Nutrition Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Type:
5.2.1. Machine Learning
5.2.1.1. Supervised Learning
5.2.1.2. Unsupervised Learning
5.2.1.3. Reinforcement Learning
5.2.2. Deep Learning
5.2.2.1. Convolutional Neural Networks (CNN)
5.2.2.2. Recurrent Neural Networks (RNN)
5.2.2.3. Generative Adversarial Networks (GAN)
5.2.2.4. Transformers
5.2.3. Natural Language Processing (NLP)
5.2.3.1. Sentiment Analysis
5.2.3.2. Language Translation
5.2.3.3. Chatbots and Virtual Assistants
5.2.4. Computer Vision
5.2.4.1. Image Classification
5.2.4.2. Object Detection
5.2.4.3. Facial Recognition
5.2.4.4. Video Analysis
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:
6.2.1. Meal Planning and Recommendations
6.2.1.1. Personalized Meal Plans
6.2.1.2. Recipe Suggestions
6.2.1.3. Dietary Restriction Considerations
6.2.2. Nutrient Analysis
6.2.2.1. Nutritional Deficiency Identification
6.2.2.2. Dietary Recommendations
6.2.2.3. Nutrient Optimization
6.2.3. Personalized Supplementation
6.2.3.1. Supplement Recommendations
6.2.3.2. Dosage and Timing Suggestions
6.2.3.3. Tracking and Monitoring
6.2.4. Allergen and Sensitivity Detection
6.2.4.1. Allergen Identification
6.2.4.2. Alternative Ingredient Suggestions
6.2.4.3. Personalized Dietary Restrictions
6.2.5. Health Monitoring
6.2.5.1. Biometric Data Analysis
6.2.5.2. Real-time Health Feedback
6.2.5.3. Behavioral Pattern Recognition
Chapter 7. Market Segmentation 3: By Provider Estimates & Trend Analysis
7.1. By Provider & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following By Provider:
7.2.1. Startups and Small Companies
7.2.1.1. AI-Powered Nutrition Startups
7.2.1.2. Innovative Tech Solutions
7.2.2. Established Tech Companies
7.2.2.1. Technology Giants
7.2.2.2. AI Platform Providers
7.2.3. Healthcare and Wellness Organizations
7.2.3.1. Hospitals and Medical Centers
7.2.3.2. Wellness Clinics and Centers
Chapter 8. Market Segmentation 4: by End User Estimates & Trend Analysis
8.1. By End User & Market Share, 2024 & 2034
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following By End User:
8.2.1. Individuals
8.2.1.1. Personalized Nutrition Apps
8.2.1.2. Smart Devices and Wearables
8.2.2. Fitness and Wellness Centers
8.2.2.1. Personal Trainers and Coaches
8.2.2.2. Nutritionist Services
8.2.3. Healthcare Providers
8.2.3.1. Hospitals and Clinics
8.2.3.2. Telehealth Platforms
8.2.4. Food and Beverage Industry
8.2.4.1. Food Manufacturers
8.2.4.2. Restaurants and Food Services
Chapter 9. Artificial Intelligence in Personalized Nutrition Market Segmentation 5: Regional Estimates & Trend Analysis
9.1. North America
9.1.1. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.1.2. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.1.3. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.1.4. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.1.5. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.2. Europe
9.2.1. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.2.2. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.2.3. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.2.4. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.2.5. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.3. Asia Pacific
9.3.1. Asia Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.3.2. Asia Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.3.3. Asia-Pacific Thermal Cyclers Asia-Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.3.4. Asia-Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.3.5. Asia Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.4. Latin America
9.4.1. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.4.2. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.4.3. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.4.4. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.4.5. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.5. Middle East & Africa
9.5.1. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.5.2. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.5.3. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.5.4. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.5.5. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles
10.2.1. Nutrino Health Ltd.
10.2.2. DayTwo Ltd.
10.2.3. Lumen
10.2.4. Nutrigenomix Inc.
10.2.5. Viome
10.2.6. Foodvisor
10.2.7. Baze Labs
10.2.8. GenoPalate
10.2.9. Habit
10.2.10. Nutraceutical Corporation
10.2.11. Nutrafol
10.2.12. Zoe
10.2.13. Healbe
10.2.14. FitGenie
10.2.15. Level Foods Inc.
10.2.16. Nutrunity
10.2.17. NutriAI
10.2.18. NutrinoTech
10.2.19. AIMEE Health
10.2.20. NutriMe
10.2.21. PreBiomics
10.2.22. NutrEval
10.2.23. NutriAdmin
10.2.24. Nutrissential
10.2.25. NutriPredict
10.2.26. Other Prominent Players
AI in Personalized Nutrition Market- By Type
AI in Personalized Nutrition Market- By Application
AI in Personalized Nutrition Market- By End User
AI in Personalized Nutrition Market- By Provider
AI in Personalized Nutrition Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
