Design of an AI-Driven Smart Restaurant Menu System for Personalized Calorie Optimization Using Health Profiles

Abstract

The growing consumption of unhealthy food and lack of dietary awareness have led to increased health issues such as obesity, diabetes, and cardiovascular diseases. Traditional restaurant menus provide static information and do not consider individual health conditions or nutritional requirements. This paper proposes an AI-driven smart restaurant menu system that delivers personalized food recommendations based on user health profiles. The system collects user inputs such as age, weight, health conditions, and dietary goals, and processes this data using machine learning algorithms to suggest optimal food choices. It integrates a nutritional database, real-time calorie tracking, and intelligent recommendation mechanisms to provide healthier alternatives and alerts. Additionally, the system utilizes smart digital displays or QR-based interfaces for seamless user interaction. The proposed system aims to promote healthier eating habits, enhance user awareness, and support balanced dietary decisions in real-time restaurant environments

Introduction

This paper proposes an AI-powered smart restaurant menu system designed to improve dietary decision-making and promote healthier eating habits. It addresses the problem that modern restaurant menus are static and often lack nutritional information, which contributes to health issues like obesity, diabetes, and heart disease.

The system uses machine learning and nutritional databases to provide personalized food recommendations based on user health profiles, including age, weight, dietary goals, and medical conditions. Unlike traditional food apps, it integrates health-aware decision-making directly into the ordering process.

The architecture includes four main layers:

• User interaction layer (QR codes or smart screens for input)
• Processing layer (handles data flow and validation)
• AI recommendation engine (generates personalized suggestions using nutritional analysis)
• Data storage layer (stores food and user data in a scalable database)

The methodology works by calculating a user’s daily calorie needs, analyzing food nutritional values, and categorizing items as recommended, moderate, or to avoid. The system also tracks real-time calorie intake and suggests healthier alternatives when needed.

Conclusion

This paper presented an AI-driven smart restaurant menu system that provides personalized calorie optimization based on user health profiles. The proposed system effectively combines artificial intelligence, nutritional data analysis, and smart interfaces to improve food selection decisions. By offering real-time recommendations and health-aware suggestions, the system enhances user awareness and promotes healthier lifestyles. The approach demonstrates strong potential for practical implementation in modern restaurant environments and contributes to the advancement of intelligent food systems.

References

[1] World Health Organization, “Healthy Diet Guidelines,” 2020. [2] J. Smith et al., “AI in Healthcare Systems,” IEEE, 2019. [3] F.Pedregosa et al., “Scikit-learn: Machine Learning in Python,” 2011. [4] U.S. Department of Agriculture, “FoodData Central Database,” 2022. [5] G. James et al., “An Introduction to Statistical Learning,” Springer, 2013. [6] K. Murphy, “Machine Learning: A Probabilistic Perspective,” MIT Press, 2012. [7] IEEE, “IoT-Based Smart Systems,” 2021.

Copyright

Copyright © 2026 Sayyad Chandan, Ponnada Kumara Swamy, Punyamanthula Mohana Durga Niharika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.