I proposed the topic and led a group of 5 to work on this project. Our study aims to develop an advanced music genre classification system with the objective of enhancing personalized recommendation mechanisms and user experience. By employing K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks (CNN, RNN), the comprehensive classification model is capable of accommodating a wide spectrum of music genres. The dataset comprises audio recordings of songs, evaluated through an array of attributes such as spectral information, rhythmic patterns, along with multiple other extracted features in CSV files. The efficacy of our proposed music genre classification system is gauged through numerous evaluation metrics, enabling the selection of the most efficient model and optimizing models' classification performance and their capability to accurately predict music genres. By demonstrating high accuracy and robustness, our tool exhibits its effectiveness and dependability and promising improvements in music recommendation systems.