Music plays a significant role in our day-to-day life, and selecting appropriate songs can enhance the experience. This paper describes an intelligent music recommendation system that applies machine learning to recognize what users prefer and recommend music that they will like. It incorporates various approaches, including considering user decisions and music attributes, to enhance suggestions. The system adapts based on user actions and refines recommendations over time. Findings indicate that this method provides easier and more precise music discovery. This paper emphasizes how technology can assist in providing a higher quality and better personalized music experience.