In this research work, we propose Thrive-Dine, an artificial intelligence-based mobile application for personalized nutrition care. This application implements using machine learning algorithms such as, Genetic Algorithms to create personalized diet charts and Reinforcement Learning to adaptively adjust meals—to provide user-specific recommendations based on user data such as age, gender, weight, diet, and activity level. The most essential technologies that have been integrated in Thrive-Dine are FastAPI for efficient backend computation, Streamlit for dynamic visualizations of data, Flutter for adaptive front end, and Spring Boot for easy handling of databases. The most prominent feature of Thrive-Dine is its dynamic meal recalculation functionality, which recalculates the future meals dynamically when it senses “cheat meals” in order to ensure that the user is never left hungry but stays on track. Previously, Personalized Diet Recommendation System using machine learning was designed mainly with individual nutritional advice and overall well-being. In this project, the machine learning model is built for the personalized health and nutrition recommendations by using different input parameters. Thrive-Dine also addresses the limitations of current applications like ChatDiet, whose personalization is sensor-wearable-based, and other apps, which does not address meal planning personalization but only food analysis. By converging AI and machine learning, Thrive-Dine completes the gap between scientific nutrition counselling and consumer access. Future growth will involve leveraging wearable technology, growing AI algorithms, and adding gamification to continue increasing user participation and health benefit.