The rapid growth of e-commerce necessitates smarter shopping experiences. This project, Smart Fashion Shopping Based on AI and Full-Stack Integration, leverages AI-powered recommendations to enhance user engagement. Developed using Python Django with MongoDB integration, the platform provides personalized fashion suggestions based on gender, category, color, season, and usage. A Kaggle dataset containing diverse fashion items is processed using machine learning models (TensorFlow, PyTorch, Scikit-learn) to generate accurate recommendations. The system dynamically adjusts suggestions based on trends and user input, ensuring an optimized shopping experience. Full-stack integration combines a responsive front-end UI with a robust backend, delivering seamless user interaction. By incorporating AI-driven insights, the platform enhances product discoverability, streamlining fashion retail.