AI-Driven Smart Food Ordering System with Personalized Nutrition Recommendations using Conversational Interface

With the increasing popularity of online food ordering platforms, there remains a significant gap in delivering personalized and health-conscious food recommendations. This paper presents a Smart Food Ordering System that integrates AI-driven personalization based on individual user health data. The proposed system combines natural language processing (NLP) with FastText embeddings for intent classification and chatbot interaction, enabling users to place food orders through a conversational interface. Personalized recommendations are generated by analyzing user-specific health parameters such as age, weight, dietary restrictions, and fitness goals. The system is developed using Flask for the web interface and MongoDB for data storage, with additional modules for real-time order tracking, payment processing, and geolocation-based delivery validation. This integrated approach not only enhances user experience but also promotes healthier food choices. Experimental results demonstrate the system’s effectiveness in accurately understanding user intent and generating contextually relevant, healthoptimized food recommendations