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 able to quantify the possibility of where landslides are likely to occur, which play a significant role in formulating disaster prevention measures and mitigating future risk. Since opinion-driven models are hard to quantify and often depend on subjective judgments, the accuracy and precision of landslide susceptibility models is now evolving from opinion-driven models and statistical learning toward increasing 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 (TGRA) are 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 landslide susceptibility predicting accuracy and efficiency. 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.