Journal of Intelligent Computing Fangfang Zhou 17 1 2026 https://doi.org/10.6025/jic/2026/17/1/26-36 https://www.dline.info/jic/fulltext/v17n1/jicv17n1_3.pdf The document "Music, Artificial Intelligence, and Deep Learning: A Review of Concepts and Genre Classification Approaches" provides a comprehensive overview of AI's transformative role in music information retrieval (MIR), with a focus on music genre classification (MGC). It outlines key AI applications in music including genre classification, recommendation, melody extraction, therapy, and generation and traces the evolution from traditional machine learning (e.g., kNN, SVM) to deep learning models (e.g., CNNs, LSTMs). The review emphasizes that while traditional methods rely on handcrafted audio features like MFCCs, chroma, and rhythm histograms, deep learning enables automatic feature learning from raw or time- frequency representations, yielding significantly higher accuracy (up to 97.8% in recent studies). Tables compare model performance, feature extraction techniques, and trade offs between interpretability and scalability. The paper acknowledges persistent challenges, such as capturing long term harmonic structure and the data hungry nature of deep models. It highlights innovations like Shuai Yu's NHAN-GAF for melody extraction and hybrid architectures (e.g., CNN-LSTM) that integrate temporal and spectral modeling. The proposed AI-based music processing pipeline spanning audio acquisition, preprocessing, feature extraction, and deep modeling illustrates an end to end workflow for modern MIR systems. The review concludes that AI, intense learning, enhances rather than replaces human creativity, and calls for future work on musicaware networks, attention mechanisms, and larger, diverse datasets to improve robustness and interpretability in real world applications.