Geomembranes (GM) have been extensively used for waterproofing applications, and often they are in contact with soil materials or other geosynthetics for mechanical protection. Strength evaluation at the interface between the GM and the contact material is fundamental to ensure a good design that guarantees a low probability of this interface failure. This paper analyses and compares four Artificial Neural Network (ANN) models (varying the number of inputs and hidden layers) and the Random Forest (RF) technique to predict sand/GM interface shear strength based on 495 results from previous investigations. All models were optimized with the Differential Evolution (DE) algorithm. The Coefficient of Determination (R2) and root mean squared error (RMSE) were set as evaluation criteria for the accuracy of the developed models. The results show that RF performs best as a prediction tool for the data analysed. Data correlation and RF feature importance analysis were also conducted, establishing GM asperity height as the most significant variable for the collected data. The results show the great potential of Machine Learning applications for predicting the interface shear strength between sand and geomembranes in geotechnical engineering constructions.