The study investigates the interpretability and explainability of machine learning models in geotechnical engineering, focusing on case studies on rock mass quality and rock type predictions from drilling data. By applying permutation feature importance, Shapley values, direct model interpretation, and partial dependency plots, features such as overburden thickness, tunnel width, feeder pressure, and penetration rate were identified as significant influencers on model predictions. These insights ensure the models’ predictions are consistent with geotechnical physical expectations, thus improving their practical applicability. The research underscores the necessity for transparent and interpretable machine learning solutions in geotechnical engineering, enhancing the reliability and application of such models in critical construction operations.