In order to improve the accuracy of geological hazard susceptibility assessment, Fuyang District in Hangzhou, Zhejiang Province was taken as the research area and a random forest method was proposed for evaluating geological hazard susceptibility, considering buffer zone optimization strategies. Firstly, nine evaluation factors were selected: normalized difference vegetation index, distance to roads, distance to faults, rainfall during the flood season, slope, aspect, ruggedness, distance to water systems, and lithology. Multicollinearity analysis was conducted to ensure the independence of the factors. Secondly, buffer zones of 0.5 km, 1 km, 1.5 km, and 2 km were constructed. Negative sample points were generated using random sampling to avoid cross-contamination between positive and negative samples, enhance sample representativeness, and improve the model's discrimination capability. An additional set of random sampling points without buffer zones was also established for comparison. The random forest algorithm was then used to train and test the geological hazard susceptibility model. Results indicated that the buffer zone optimization strategy significantly improved the model's predictive accuracy and that there was an optimal boundary for the buffer zone. The model's AUC(area under curve) value was highest at 0.815 for the 1 km buffer zone, indicating that negative samples collected within this buffer zone could more accurately distinguish geological hazard characteristics. Finally, based on the susceptibility evaluation results of the optimal buffer zone and the random forest model, high susceptibility areas were mainly concentrated in the mountainous regions in the northwest and southeast. The frequency ratio increased with the susceptibility level, validating the scientific validity of this method. This approach can provide a basis for geological hazard prevention and control in Fuyang District.