This project aims to develop an advanced recommendation system for YouTube contents using Python programming language. Leveraging machine learning algorithms, the system will analyse user preferences and movie features to provide personalized recommendations, thereby enhancing user engagement and satisfaction on the platform. By utilizing the YouTube API or publicly available datasets, comprehensive movie metadata will be collected and pre-processed to ensure data quality. The recommendation system will encompass various algorithms including collaborative filtering, content- based filtering, and hybrid approaches, implemented using Python libraries such as scikit-learn and surprise. Evaluation of the system's performance will be conducted through metrics such as accuracy, precision, recall, and F1-score. A user-friendly web interface will be developed using Flask or Django, allowing users to interact with the system, rate movies, and receive recommendations. Finally, the system will be deployed on a web server or cloud platform for seamless accessibility, marking a significant contribution to the field of recommendation systems.