In the context of the digital music industry, accurate classification of music into genres and the ability to recommend appropriate genres for a user greatly improves usability of various streaming platforms. Several traditional machine learning approaches that rely on metadata face significant limitations, such as inconsistencies in the data, the growing diversity of musical styles, and a lack of focus on the actual musical content of songs. These shortcomings often result in suboptimal performance, particularly in recommendation systems. In response to these issues, this work proposes Deep Tune Network (DTN), a deep learning system for automated genre analysis and discovery of music based on similar acoustic patterns. This system uses Convolutional Neural Networks (CNNs) and Mel-frequency cepstral coefficients (MFCCs) to identify the repetitive patterns inside audio signal needed to classify music into different genre. The model achieves a maximum test accuracy of 93.01%, demonstrating its reliability in real-world applications. Additionally, a cosine similarity-based recommendation system is implemented to suggest acoustically similar songs, bridging accurate genre classification with personalized playlist curation.