Music Recommendation Systems are algorithms designed to suggest songs or artists to users based on their listening habits and preferences. These systems analyze data such as user interactions, song characteristics, and trends to provide personalized recommendations. Popular platforms like Spotify and Apple Music utilize these systems to enhance user experience and keep listeners engaged. These systems often employ techniques like collaborative filtering, which considers the preferences of similar users, and content-based filtering, which focuses on the attributes of the music itself. By combining these methods, Music Recommendation Systems aim to introduce users to new music they are likely to enjoy, fostering musical discovery.