In this paper, we investigate a comprehensive approach to music genre classification, utilizing a combination of deep neural networks and machine learning algorithms. Our research aims to advance the understanding of music’s impact on our lives and develop methodologies to create diverse and engaging musical experiences tailored to individual preferences. We begin by extracting relevant features from a large and diverse collection of music samples from different genres. These features, encompassing spectral properties, rhythmic patterns, and tonal characteristics, serve as the foundation for our genre classification and generation models. We employ deep neural networks and machine learning algorithms to effectively classify music genres by capturing the distinct characteristics of each genre. While not claiming state-of-the-art performance, our approach demonstrates promising outcomes in classification tasks, showcasing its potential to enhance music-related applications such as recommendation systems and creative tools for composers.