Music recommendation systems (MRS) play a crucial role in navigating extensive music libraries, helping users discover content that aligns with their preferences while addressing challenges such as decision fatigue and overload. This paper explores the evolution of MRS, emphasizing the limitations of traditional approaches like Collaborative Filtering and Content-Based Filtering, which struggle with issues such as cold-start problems, data sparsity, and popularity bias. Hybrid systems, which integrate these methodologies, have emerged as a robust solution, offering improved accuracy, diversity, and personalization. The analysis focuses on advanced hybrid techniques, including graph-based models, multimodal data integration, and artificial intelligence methods such as deep embeddings and adversarial learning. These innovations address critical challenges, including the semantic gap and scalability, while promoting fairness and diversity through metrics that extend beyond accuracy. Furthermore, emerging trends, such as socially motivated frameworks and context-aware recommendations, are examined for their potential to redefine user engagement and enhance the overall recommendation experience. The findings underline the scalability and robustness of hybrid systems, particularly graph-based methodologies, as the future of MRS. However, significant challenges remain, including the optimization of computational efficiency and the creation of equitable recommendation ecosystems. This study concludes by identifying future directions, including real-time adaptability, multimodal integration, and the development of fairness-aware frameworks. These insights underscore the need for continued innovation to meet evolving user needs and technological advancements in the field of music recommendation systems.