Music Information Retrieval (MIR) is the task of extracting high-level information, such as genre, artist, or instrumentation, from music. Genre classification is an important and rapidly evolving research areas of MIR. To date, only a small amount of research work has been done on the automatic genre classification of Nigerian songs. Hence, this study presents a new music dataset, namely the ORIN dataset, consisting of only Nigerian songs. The study dataset contains 478 Nigerian traditional songs from five genres: fuji, juju, highlife, waka and apala. The timbral texture and tempo features were mined from 30-second segments of each song using the Librosa Python library. For genre classification, the ORIN datasets were trained on different classifiers: k-Nearest Neighbor, Support Vector Machine, eXtreme Gradient Boosting (XGBoost), and Random Forest with 85–15 train-test splits. The results obtained for the five different genres indicate that XGBoost classifier is a better model, having the highest accuracy of 81.94%