An AI-Driven Framework for Personalized Dietary Recommendation and Meal Planning

Personalized dietary guidance has become an essential requirement in modern healthcare due to increasing lifestyle-related health issues. This paper presents NutriSmart, an intelligent web-based nutrition advisory system that delivers personalized dietary recommendations by integrating machine learning and generative artificial intelligence. The proposed solution employs a three-tier architecture consisting of a dynamic, conversational user interface and a backend processing layer powered by machine learning model and a large language model (LLM). Nutritional data are sourced from the USDA FoodData Central Foundation Foods database, which provides comprehensive information on food composition and nutrient profiles. A nutrient KNN-cluster scoring mechanism is used to evaluate and assign food recommendations based on individual user requirements. The machine learning model generates initial dietary predictions, which are subsequently combined with contextual prompts and passed to Google Generative AI to produce real-time, personalized meal recommendations. Designed with a conversational interaction paradigm, NutriSmart enables users to query dietary needs naturally and receive tailored advice in an intuitive and user-friendly manner. By combining data-driven prediction with generative reasoning, the proposed system demonstrates potential as a scalable and intelligent nutrition guidance platform for personalized health and diet management.