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In the rapidly evolving world of digital commerce, providing personalized customer experiences has become essential for improving user satisfaction and boosting sales. With millions of products and users interacting on platforms like Amazon and Flipkart, navigating large catalogs without intelligent support often leads to inefficiency and dissatisfaction. This project proposes a machine learning-based personalized product recommendation system that combines content-based filtering and collaborative filtering techniques to deliver accurate, dynamic, and user-specific suggestions. The system preprocesses user data such as ratings, purchase history, and browsing behavior, and employs hybrid recommendation models to address challenges like data sparsity and cold-start problems. It is deployed using a Streamlit web application, allowing users to interactively receive recommendations in real time. Evaluation metrics including precision, recall, F1-score, and RMSE validate the model’s effectiveness. By bridging the gap between user needs and business offerings, the proposed system supports intelligent, scalable, and user-centric e-commerce platforms.These approaches tend to be generic, static, and lack the adaptability needed to reflect evolving user interests.