Mineral exploration faces challenges from complex geological architectures and subtle geophysical expressions of mineralization. This study proposes a joint gravity–magnetic machine learning framework integrating magnetic anomaly, analytic signal, gravity, and Source Parameter Imaging depth estimates to enhance mineralization prediction in Nigeria's Middle Benue Trough and adjoining basement terrain. Five supervised algorithms were evaluated, with Random Forest achieving the highest performance (accuracy=0.954, precision=0.889, recall=0.819, macro-F1=0.850, ROC-AUC=0.978). Correlation analysis revealed low feature redundancy, while unsupervised clustering confirmed structural partitions co... More >