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Paper ID:IJSREM68217

AI Diet and Nutrition Recommendation System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68217

Volume:

10 |

10 |

Oct 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

M

M.Saranya

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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A

A.Sowndharya

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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G

G.Thikanthra.

Department of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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Abstract

1. The AI-Based Diet and Nutrition Recommendation System is an intelligent healthcare application designed to provide personalized dietary recommendations based on individual health profiles, nutritional requirements, food preferences, and lifestyle characteristics. The system aims to overcome the limitations of conventional dietary planning methods by integrating Artificial Intelligence (AI), nutritional analysis, and automated meal planning. It helps users make informed food choices by providing suitable dietary suggestions according to their individual requirements and health objectives. 2. The proposed system collects essential user information, including age, gender, height, weight, physical activity level, dietary preferences, and fitness goals. Based on these parameters, the system estimates daily calorie requirements and evaluates nutritional needs. It subsequently generates personalized meal recommendations by considering calorie intake, protein requirements, and other relevant nutritional factors. The recommendation process is designed to provide suitable food options while supporting different objectives, such as weight management, balanced nutrition, and general wellness. 3. The primary objective of the AI-Based Diet and Nutrition Recommendation System is to make personalized nutrition guidance more accessible, efficient, and convenient. By combining individual health information with nutritional analysis, the system provides a technology-driven approach to dietary management. The recommendations are intended for general wellness and nutritional awareness and should be validated against established dietary guidelines. Professional healthcare advice remains necessary for individuals with medical conditions or specialized dietary requirements.

KEYWORDS

health profiles nutritional requirements food preferences and lifestyle characteristics

Cite This Article

M.Saranya, A.Sowndharya, G.Thikanthra., "AI Diet and Nutrition Recommendation System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-7, Oct 2026.

Full Article/Conclusion

The AI-Based Diet and Nutrition Recommendation System provides an intelligent approach to personalized dietary planning by integrating user health profiles, nutritional analysis, and automated meal recommendations. The proposed system considers individual characteristics, dietary preferences, physical activity levels, and health objectives to estimate nutritional requirements and suggest suitable food options. The integration of calorie tracking, meal management, and progress monitoring enables users to understand their dietary habits and make informed food choices. The proposed approach aims to overcome the limitations of conventional dietary planning methods by providing a centralized platform for personalized nutrition management. By combining Artificial Intelligence techniques with nutritional information, the system offers opportunities to improve the accessibility and convenience of dietary guidance. The modular design also supports future enhancements, including advanced machine learning algorithms, real-time fitness data integration, and adaptive meal recommendations.

References

[1]                  A. Sharma and R. Gupta, “Web-Based Internship Management System,” International Journal of Computer Applications, vol. 182, no. 12, pp. 15–20, 2018.   [2]                  S. Kuma[1] K. Kalpakoglou, L. Calderón-Pérez, N. Boqué, M. Guldas, Ç. Erdoğan Demir, L. Gymnopoulos, and K. Dimitropoulos, “An AI-based nutrition recommendation system: Technical validation with insights from Mediterranean cuisine,” Frontiers in Nutrition, vol. 12, Art. no. 1546107, 2025, doi: 10.3389/fnut.2025.1546107.   [3]                   I. Papastratis, D. Konstantinidis, P. Daras, and K. Dimitropoulos, “AI nutrition recommendation using a deep generative model and ChatGPT,” Scientific Reports, vol. 14, Art. no. 14620, 2024, doi: 10.1038/s41598-024-65438-x.    [4]                  [3] L. Yang, C.-K. Hsieh, H. Yang, J. P. Pollak, N. Dell, S. Belongie, C. Cole, and D. Estrin, “Yum-Me: A personalized nutrient-based meal recommender system,” ACM Transactions on Information Systems, vol. 36, no. 1, Art. no. 7, 2017, doi: 10.1145/3072614.    [5]                  [4] “Principles and validations of an artificial intelligence-based recommender system suggesting acceptable food changes,” The Journal of Nutrition, vol. 153, no. 2, pp. 598–604, 2023, doi: 10.1016/j.tjnut.2022.12.022.   [6]                  [5] Z. B. Ter, P. Naveen, and J. Jayapradha, “Generative AI-based meal recommender system,” Journal of Informatics and Web Engineering, 2025, doi: 10.33093/jiwe.2025.4.2.20.

Editorial & Review Transparency

Publication Date

10/24/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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