Through clever Food recommendations, the AI-driven Dining Management System with meal Order Recommendation optimises restaurant operations and improves the dining experience for patrons. A machine learning-based engine for recommendations is integrated into the system to examine user preferences, past orders, and behavioural tendencies. Flask is used for backend processing. The system offers tailored food recommendations by utilising strategies like filtering based on content, filtering with others, or a combination of the two, which raises customer happiness and engagement. While restaurant managers can effectively handle orders and inventory, patrons can easily peruse the menus, place purchases, and receive personalised recommendations thanks to the platform. Incorporating AI-powered suggestions improves user engagement while also boosting revenue and operational effectiveness. This study shows how data-driven suggestions can transform food services by examining the use, performance assessment, and effects of artificial intelligence in the restaurant sector.