This project presents a personalized music recommendation system that suggests songs based on user preferences using Spotify data. It analyzes audio features such as danceability, energy, tempo, and valence to identify similar tracks. The system uses machine learning techniques like K-Means clustering and cosine similarity to generate recommendations, while integrating the Spotify API to fetch up-to-date song data. Built with a Next.js frontend and a modern backend, it provides an interactive interface for users to discover music, demonstrating the application of unsupervised learning and web technologies in recommendation systems.