Abstract: Valley-type debris flows in the Xinjiang region occur frequently and cause significant hazards. This study takes Jiangjungou in Altay as the research object to explore its formation mechanism and analyze the effectiveness of prevention and control measures. The formation mechanism was analyzed through field investigations, data interpretation, and machine learning. Combined with the characteristics of the 2003 disaster, MassFlow software was used to simulate the working conditions before prevention, after prevention, and when the project fails, and the prevention and control effects were evaluated by comparing the scouring and silting ranges. The results show that the debris flow in Jiangjun gully is a medium-sized disaster developed from mountain torrents, and its disaster-forming process presents a chain reaction: heavy rainfall enhances surface runoff, triggers gully bed incision and bottom scouring, activates the material sources in the tributaries and the main gully, and through continuous supply of materials by gravity erosion and collapse, finally forms a high-impact debris flow, which causes disasters through silting and river blocking. The initial effect of the basin management project was significant, and it still has the ability of prevention and control at present, but it has been continuously declining under the influence of natural factors and engineering aging. The main reason for the inaccuracy of monitoring and early warning is that the threshold setting ignores key factors such as antecedent rainfall. Based on this, it is suggested that the prevention and control of debris flow in Jiangjun gully should rely on machine learning to dynamically iterate parameters, construct an I-D-E ternary threshold model, and introduce the reduction factor of prevention and control engineering effects to correct the rainfall and mud level thresholds, so as to adapt to the unique geological and hydrological conditions in Xinjiang and improve the accuracy of early warning.