Music has held an important place in human life throughout history and has continued to exist as a significant form of expression in cultural, social, and artistic contexts. With the advancement of technology, the ways in which music is produced, distributed, and consumed have undergone substantial transformation. In particular, with the widespread adoption of digital technologies, music listening platforms have evolved, and a vast number of musical works have become accessible in digital environments. In addition, the use of digital tools and software in music production processes has increasingly expanded.

The large volume of music data available in digital environments has made processes such as analyzing and classifying these data, discovering new music, and developing recommendation systems based on listener preferences increasingly important. In this context, machine learning methods play a significant role in the processing and interpretation of music data. Especially on digital music platforms where Western music is predominantly represented, numerous studies in the literature focus on topics such as the identification of musical genres, the analysis of song popularity levels, and the automatic classification of musical works.

In this section, the structure and representation methods of music data are examined from the perspective of machine learning (ML). Furthermore, recent research and example studies on the application of machine learning techniques in music analysis are reviewed. In this regard, the section aims to provide guidance for both researchers and practitioners by presenting a theoretical conceptual framework as well as practical examples.

In conclusion, this section serves as a concise resource that provides both theoretical knowledge and practical guidance for researchers and practitioners aiming to achieve specific research objectives using music data. By demonstrating how machine learning methods can be effectively applied in music research and applications, it also establishes a fundamental reference framework for future studies.