The complicated topographic status of Linxia County has led to the development of landslide disasters, posing a major threat to local residents’ lives and livelihoods as well as hindering the advancement of project construction. Thus, it is crucial to choose an accurate and effective machine learning technique for assessing Linxia County’s landslide susceptibility. Firstly, we selected 1 718 landslide samples based on remote sensing images and field investigation, and chose 16 influencing factors of landslide disaster to construct an evaluation system. Then the performance of the LightGBM model and the widely used mainstream machine learning models are evaluated from perspectives of prediction accuracy, running time and etc. Finally, the LightGBM model is utilized to evaluate the susceptibility of landslides in Linxia County using the confusion matrix categorization method. Results show that the main influencing factors of landslide in Linxia County are surface vegetation and topographic and geomorphic factors, among which land cover is the most influential factor. The prediction accuracy of LightGBM model can reach 0.931, and it takes merely 11.7 seconds to run, establishing an excellent performance of high precision and enhanced operational efficiency. On the extracted data set, the prediction accuracy, degree of calibration and classification results of the LightGBM model are better than those of random forest. According to the confusion matrix categorization, the landslide distribution is more concentrated in the high landslide-prone areas and extremely high landslide-prone areas, with 18.22% of the areas hosting 86.86% of landslide disaster locations. The evaluation results of landslide susceptibility are consistent with the distribution and development of landslides in the study area, which can provide some guidance for local engineering construction as well as disaster prevention and mitigation.