Artificial Intelligence and Machine Learning (AI/ML) are gaining increasing interest due to their capacity to increase precision and productivity in the current big data era. Machine learning has indicated its robustness in geosciences, particularly rock-type classification. Lithological classification in the traditional way has raised critical concerns and the need to curb the limitations it breeds, such as time consumption and subjective results. The gold mineralization occurrence is structurally controlled in the Obuasi Gold district of Ghana. It exhibits complex patterns and relationships that may not be readily discernible through traditional methods, leading to missing out on discovering new resources or potential exploration targets. Consequently, this chapter attempts to create a predictive model by exploring the best machine-learning algorithms to predict rock types in the Obuasi Gold District using X-ray fluorescence (XRF) geochemical data. Comparative predictive modelling was established using four supervised classification algorithms: Gradient Boosting (GBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM) and Random Forest (RF). The acquired XRF data was integrated with the model using the Google Collaboratory cloud-based platform. Results show that the performance evaluation of the models indicated SVM as the best algorithm for deployment with a Classification Accuracy (CA) of 0.902. Therefore, ML algorithms have been a great tool in rock-type classification, whereby SVM emerged as the best in the case of the Obuasi Gold District. However, it is encouraged to understand the geology of a particular area before employing the tool and the datasets must be balanced to yield good results and avoid model overfitting.