This study applied to evaluate landslide susceptibility using four data mining models including, “Generalized Linear Model (GLM)”, “Maximum Entropy (ME)”, “Artificial Neural Network (ANN)”, and “Support Vector Machine (SVM)” in Cherikabad Watershed in Urmia City, Iran. In particular, Shannon entropy was used to assess the intercomparison of factors’ classes. Eleven factors including, elevation, slope angle, slope aspect, geological formation, annual mean rainfall, land use/ land cover, distance to the village, distance to faults, distance to roads, distance to streams, and NDVI used in the current study. Landslide inventory map was identified using Google Earth imagery, extensive field surveys, and scrutinizing archived data. The produced landslide susceptibility maps were evaluated by the AUROC index. The results of performance metrics revealed that the Shannon entropy with an AUROC of 0.879 proved highly reliable and so is the intercomparison analysis of factors’ classes derived from it. Additionally, the goodness-of-fit of the GLM, ME, ANN, and SVM models were 0.763, 0.740, 0.926, and 0.924, while their predictive powers were 0.751, 0.727, 0.917, and 0.935, respectively. Hence, the results indicated that the SVM model can be introduced as the superior model for the study area based on which the most critical factors affecting landslides were found to be elevation, annual mean rainfall, and distance to the village. The results of this work are of great use for land use planning in landslide-prone areas with similar geo-topological, geomorphological, and climatic conditions.