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The Fitness and Nutrition Recommendation System is an intelligent mobile application designed to deliver personalized health guidance through artificial intelligence and deep learning. The system combines fitness tracking and nutrition management into a single, user friendly platform. It utilizes a Convolutional Neural Network (CNN ) model, implemented with TensorFlow Lite, to analyze users’ body images and classify their body composition as healthy or overweight. Based on this analysis, along with personal parameters such as BMI, age, and health goals, the app generates customized workout and diet plans tailored to each individual. Beyond personalized recommendations, the system integrates several interactive features such as an AI-powered chatbot, progress tracking, and goal management tools to enhance user engagement. It also includes an e-commerce component for purchasing fitness equipment and nutritional supplements. By performing AI inference locally on the device, the application prioritizes data privacy while maintaining high-speed performance. This project aims to bridge the gap between fitness technology and healthcare analytics, promoting a data-driven approach to health improvement. Although the app relies on user input for accurate results and internet access for updates, it offers a practical, accessible, and efficient way for users to manage their fitness and nutrition goals in an integrated environment.