We addressed the challenges of mapping lithological units on the ground and pro‐ posed a framework to overcome them. Our framework presented in a Jupyter notebook is an open‐source community tool for mapping lithological units by using multi‐ or hy‐ perspectral data. This notebook can significantly enhance the ability of exploration geolo‐ gists to map lithological units. It can be considered a fast, reliable, and low‐cost approach for generating a remote‐sensing evidential layer and delineating favorable loci for pre‐ cious mineral deposits at any stage of an exploration program. The framework can be improved by optimizing SVM, MLP, and CNN hyperparameters. Moreover, other ML methods, such as random forest, naive Bayes, k‐nearest neighbors, and minimum dis‐ tance, can be added to this framework to compare their efficiency with other methods.