Accurately discerning lead-zinc open pit mining areas using traditional remote sensing methods is challenging due to spectral signature class mixing. However, machine learning (ML) algorithms have been implemented to classify satellite images, achieving better accuracy in discriminating complex landcover features. This study aims to characterise various ML models for detecting and classifying lead-zinc open pit mining areas amidst surrounding landcover features based on Sentinel 2 image analysis. Various associated band ratios and spectral indices were integrated with processed Sentinel 2 reflectance bands to enhance detection accuracy. Suitable bands highlighting lead and zinc mine areas were identified based on optimal index factor (OIF) analysis and various deep learning-based stacked encoders. Furthermore, 15 different ML classifiers were tested to identify optimised algorithms for accurately discriminating complex mining areas and associated landcover features. After detailed evaluation and comparison of their accuracies, the extra tree classifier (et) was the most effective, achieving an overall accuracy of 0.94 and a kappa coefficient of 0.93. The light gradient boosting machine classifier (lightgbm) and random forest classifier (rf) models also performed well, with overall accuracies of 0.937 and 0.936 and kappa coefficients of 0.925 and 0.925, respectively.