Digitalisation and the proliferation of online music listening platforms have led to the exponential growth of music data on the Internet, thus necessitating the development of automated systems for data organisation and analysis. In this context, automatic genre classification practices have become a significant approach for the efficiency of music discovery and recommendation processes. While significant progress has been made in genre classification, subgenre classification remains an under-researched area, despite its potential to provide more personalised listening experiences. This study aims to address this gap by focusing on the classification of hip-hop music subgenres, namely boombap, jazzrap and trap, utilising a comprehensive dataset comprising 750 audio files. The study extracts a total of 31 features, encompassing both spectral and psychoacoustic characteristics. Machine learning models such as Logistic Regression, K-Nearest Neighbours, Decision Tree and Random Forest are employed, along with the Artificial Neural Network, which attains the highest accuracy of 85%. The findings reveal that subgenre classification poses challenges, especially for categories such as jazzrap and boombap, which share overlapping musical characteristics. In contrast, trap with different timbral characteristics was classified with higher accuracy. This study contributes to the scant research on subgenre classification by underscoring the viability of employing deep learning techniques to enhance the precision of comprehensive datasets and intricate subgenre categorisations. Moreover, this research underscores the pivotal role of subgenre classification within the ambit of digital music platforms. The accurate identification of subgenres not only elevates the overall auditory experience for users but also facilitates the discovery of music selections that resonate closely with their individual preferences.