The main aim of this study is to use weights of evidence (WoE), logistic regression (LR), naïve Bayes (NB), and alternating decision tree (ADTree) models to draw a landslide susceptibility map in Yanchuan County, China. First, 311 landslide points were identified through historical data, aerial interpretation, and field investigation to generate landslide inventory maps. Second, the landslide points were randomly divided into two groups (70%/30%) for training and validation. Then, 16 landslide conditioning factors were selected, namely slope aspect, slope angle, elevation, topographic roughness index (TRI), slope length (SL), convergence index (CI), terrain positioning index (TPI), profile curvature, plan curvature, distance to rivers, distance to roads, lithology, soil, rainfall, land use, and normalized difference vegetation index (NDVI). Variance inflation factors (VIF), tolerance (TOL), and Pearson correlation coefficient (PCC) were used to detect potential multicollinearity problems between these factors. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and area under curve (AUC) methods. The areas under the curve obtained through WoE, LR, NB, and ADTree methods are 0.822, 0.833, 0.821, and 0.847 for the training dataset, and 0.888, 0.897, 0.898, and 0.823 for the validation dataset, respectively. The results show that the ADTree model has an overfitting state, so LR has the best balance performance. This also proves that advanced machine learning models do not necessarily perform better than traditional models. The results obtained will assist in the future identification of landslide areas to better manage and reduce the negative environmental impact of landslides.