Abstract: China is one of the countries most frequently affected by landslide disasters in the world, making landslide susceptibility assessment crucial for effective disaster prevention and mitigation. Due to variations in the adaptability of different machine learning models in different regions, in order to better carry out landslide disaster prevention and control work in Badong County, Hubei Province, ten influencing factors including slope gradient, slope direction, curvature, degree of undulation, stratigraphy, overburden, NDVI, road density, water system density, and slope structure were selected. Four different models, including Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Random Forest (RF), were used for landslide susceptibility evaluation. Three evaluation methods were used to assess the accuracy of the model: Receiver Operating Characteristic (ROC) curves, mean square error, determination coefficient, and the ratio of landslide to study area. The experimental results show that there are differences among the models in different evaluation methods. Overall, the RF model exhibits the highest accuracy and generates more reasonable susceptibility zoning maps. The susceptibility distribution maps generated by the four models are similar, with high and very high susceptibility areas predominantly located in the southern riverside area. Areas near Guandukou Town and Jiaojiawan Village along the southwest coast exhibit relatively high susceptibility. The assessment results can provide reference for landslide control in Badong County.