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.
| Report Attribute | Specifications |
| Market Size Value In 2024 | USD 4.13 Bn |
| Revenue Forecast In 2034 | USD 20.98 Bn |
| Growth Rate CAGR | CAGR of 17.8% from 2025 to 2034 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
| Historic Year | 2021 to 2024 |
| Forecast Year | 2025-2034 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type, Application, End-user, Provider |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| 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 |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
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-
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.