Humans have paid a lot of attention to environmental geological challenges in recent years. Landslides, being one of the most prevalent geological disasters, are characterized by their suddenness and destructiveness. Southwest China is prone to landslides and debris flows due to its unique geological structure. This paper uses landslides in southwest China as an example, focusing on research on landslide initiation mechanisms and outlining modern landslide monitoring devices and prediction models. Landslides are caused by external variables such as persistent precipitation, groundwater movement, and significant seismic activity, as well as interior reasons such as fine particle rearrangement and the action of positive pore water. The reduction of the friction coefficient of the shear surface, which is induced by the increase of the shear rate, the supercritical carbon dioxide and superheated steam of the shear zone, and the mineral recrystallization process on the shear surface, all have an important impact on reducing the friction coefficient of the shear surface, is a key factor in the occurrence of high-speed remote landslides. Real-time landslide monitoring using space-air-ground and acoustic emission technology, as well as the creation of machine learning-based forecast models, have aided in the research of landslide development and early warning. © 2022 Technoscience Publications. All rights reserved.