There are many mountainous areas in China, with complex terrain, weak planes and geological structures and wide distribution of geohazards. Landslides are one of the most catastrophic natural hazards occurring in mountainous areas, leading to economic loss and casualties. Landslide susceptibility models are capable of quantifying the possibility of where landslides are prone to occur, which plays a significant role in formulating disaster prevention measures and mitigating future potential risk.Since expert-based models are difficult to quantify and generally depend on the subjective judgments, the accuracy and precision of landslide susceptibility models are now evolving from expert models and statistical learning toward the promising use of machine learning methods. This study presented critical reviews on current machine learning models for landslide susceptibility investigation, an extensive analysis and comparison between different machine learning techniques (MLTs) from case studies in the Three Gorges Reservoir area was presented. In combination with field survey information as well as historical data, machine learning models were used to map landslide susceptibility and help formulate landslide mitigation strategies. The advantages and limitations of several frequently employed algorithms were evaluated based on the accuracy and efficiency of landslide susceptibility forecasting models. As the result shows, the tree-based ensemble algorithms models achieved better compared with other commonly methods of papping landslide susceptibility. Furthermore, the effect of database quality and quantity is significant, and more applications of some advanced methods (i.e., deep learning algorithms) are yet to be further explored in further researches.