Geological hazard susceptibility assessment (GSCA) is a crucial tool widely utilized by scholars worldwide for predicting the likelihood of geological disasters. The traditional information quantity model in geological disaster susceptibility evaluation, which superimposes the information quantity of each evaluation factor without considering their weights, often negatively impacts susceptibility zoning results. This paper introduces a method employing random forest (RF) empowerment information quantity to address this issue. The method involves calculating objective weights based on a parameter-optimized random forest model, assigning these weights to each evaluation factor, and then conducting a weighted superimposition of the information. Utilizing the natural discontinuity method, the resulting comprehensive information volume map was segmented. The proposed method was applied in Kang County, Gansu Province, and its performance was compared with that of traditional methods in terms of geological disaster susceptibility zoning maps, zoning of statistical disaster point density, and receiver operating characteristic (ROC) curve accuracy. The experimental findings indicate the superior accuracy and reliability of the proposed method over the traditional approach.