Abstract: Gully erosion can usually cause the serious environmental damage and economic losses. Susceptibility assessment and spatial distribution of gully erosion can provide a strong references for the targeted measures, decision-making on environmental protection, geologic disaster prevention, water resource management, and infrastructure planning. It is of great significance to better understand and manage the gully system, and then reduce potential risks and losses. The regional environment can be improved to maintain the regional food production in sustainable economic development. Much effort has focused on the prediction models of soil erosion over the past few decades. The amount of erosion on slopes has been predicted from both empirical statistical and physical genesis models. However, few models have been developed to predict the development of erosion gullies. Gully erosion is more complicated than slope erosion. Furthermore, the modeling has only focused on the shallow gully erosion. Only a few studies have been conducted on the topographic, critical and empirical model of gully morphologic features, the gully erosion prediction, and the landscape evolution. It is still lacking on general extrapolation of these models, even the susceptibility of gully erosion. Multiple factors can also be considered, such as the topography, geomorphology, soil, climate, vegetation and anthropogenic factors. A comprehensive and quantitative analysis of the influencing factors can be expected with the remote sensing satellites, resource and environmental surveys. A large amount of gully-erosion data has been accumulated to statistically modeling in this field. The susceptibility assessment of gully erosion aims to calculate the importance of the influencing factors on the occurrence of gully erosion and the prediction performance between different algorithms. It is still lacking on the innovative and promotional research in this field. In this review, the flow of research in this direction was systematically introduced to summarize the strengths and weaknesses of the statistical models under various classifications. The advance in this direction was proposed to compare the commonalities and differences in the application conditions from three aspects: the process of gully erosion, the data construction, and the application of statistical model. At the same time, future research needs to realize the application of transfer learning and time series models in the susceptibility assessment on gully erosion. The deep learning and physical mechanisms can be integrated to clarify the erosion gully development, in order to improve and strengthen the cross-disciplinary application of statistical models in soil erosion. The finding can lay the theoretical and technical foundation for regional development planning.