Machine Learning Algorithms (MLAs) have recently introduced considerable lithologic mapping. Thus, this study scrutinizes the efficacy of Artificial Neural Network (ANN), Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) over hybrid datasets including optical (Sentinel 2, ASTER, Landsat OLI and Earth-observing 1 Advanced Land Imager (ALI)), radar (Sentinel 1 and ALOS PALSAR), DEMs and their derivatives (Slope, and Aspect). The study aims to (1) monitor the effect of data dimensionality in enhancing categorization accuracy. (2) disclose the most efficient MLA and most powerful dataset in labeling rock units accurately. (3) highlight the impact of embedding topographical and radar data in lithologic classification. (4) outline the best relation between the number of training pixels and number of utilized bands, in delivering reliable allocation. To achieve these aims, we selected training and testing pixels meticulously, in concordance with a recently published geological map of the study area. We adopted a stacked vector approach for handling the implemented multi-sensor data. Results show that diversifying information sources raised the classification accuracy by approximately 10% for each classifier. SVM and MLC are much better than ANN. Slope is better than aspect and both are less qualified when compared to DEM. Sentinel 1 (C-band) and ALOS PALSAR (L-band) effects are not so different whatever the implemented polarization. Landsat OLI is less qualified in lithologic classification when compared to Sentinel 2, ASTER and ALI. The utilized training pixels should be at least 30N for (N) channels submitted to the classifiers.