To address the frequent severe rainfall disasters along the Beijing-Zhangjiakou Railway, this study employs a combined weighting analysis method integrating the Analytic Hierarchy Process and Random Forest algorithm for risk assessment and zoning to facilitate disaster warning. Utilizing data from rainfall sensor networks along the railway line, a risk model was established by assigning weights to three categories of indicators: hazard factor risk, sensitivity of disaster-pregnant environments, and vulnerability of disaster-bearing bodies. Risk levels were calculated by integrating rainfall and geological data, with visualization achieved through ArcGIS software. Experimental results identified the highest-risk areas in the Badaling-Nankou section and Qinglongqiao segment, consistent with historical disaster records. Compared with single-method AHP approaches, this hybrid method demonstrates enhanced accuracy, providing valuable references for optimizing disaster warning systems and improving emergency response mechanisms.