Music has always been an essential aspect of human culture, and the methods for its creation and analysis have evolved alongside the advancement of computational capabilities. With the emergence of artificial intelligence (AI) and one of its major goals referring to mimicking human creativity, the interest in music-related research has increased significantly. This review examines current literature from renowned journals and top-tier conferences, published between 2017 and 2023, regarding the application of AI to music-related topics. The study proposes a division of AI-in-music research into three major categories: music classification, music generation and music recommendation. Each category is segmented into smaller thematic areas, with detailed analysis of their inter- and intra-similarities and differences. The second part of the study is devoted to the presentation of the AI methods employed, with specific attention given to deep neural networks—the prevailing approach in this domain, nowadays. In addition, real-life applications and copyright aspects of generated music are outlined. We believe that a detailed presentation of the field along with pointing out possible future challenges in the area will be of some value for both the established AI-in-music researchers, as well as the new scholars entering this fascinating field.