The Huyugou river basin is a typical debris flow river basin in the Shanxi Province, which has great harm after the outbreak and seriously affects the safety of people’s lives and property. Therefore, it is urgent to carry out debris flow risk assessment. In this paper, a machine learning algorithm is implemented to assess the disaster susceptibility of each branch gully in a river basin of the Huyugou. Furthermore, its high-susceptibility branch gully and main gully were selected as the starting points of debris flow simulation for numerical simulation. The machine learning algorithm is implemented in a cloud-edge platform to minimize the model training and prediction times. Under the simulated rainfall conditions of major debris flow disasters, e.g., the one that occurred in 1996, the accuracy rate reached 84%. The results show that the debris flow susceptibility of each branch gully in the study area is mainly affected by the peak flow rate of the river basin, the length of the main gully, and the relative height difference of the river basin. The total risk area of debris flow is 1.91 × 105 m2, and the high-risk area accounts for 52.18% of the total area. It is mainly located in the upper part of the main gully accumulation area and the confluence of each channel and the main gully. The middle-risk area accounts for 36.14% of the total area, and the low-risk area accounts for less. We also observed significant reduction, from 34.68% to 36.98%, in the training and prediction times of the machine learning models when implemented over the proposed edge-cloud framework. The reappearance of debris flow in the study area is relatively accurate, which provides a certain scientific basis for the risk assessment of debris flow in the future.