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The study employs climate factors, including temperature and rainfall, alongside 12 causative factors in a Support Vector Machine (SVM) model to develop a Landslide Susceptibility Map (LSM). The Statistical Downscaling Model (SDSM) is used to derive climate change projections under two Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). Data preparation and normalization are performed using ArcGIS 10.7. Based on the results, future annual rainfall and daily temperatures are expected to rise under both scenarios, with RCP8.5 exhibiting more significant climatic changes. The LSM zonation is impacted more significantly under RCP8.5 due to the severity of climate change. The AUC, SI and Kappa values in the validation phase yielded excellent values for both the training and testing data sets, indicating reliable performance of LSM produced. These LSMs can assist local authorities in designating critical areas for monitoring and implementing an early-warning system to respond more effectively to landslide risks caused by climate change. Utilizing the LSM produced in this study has benefited policy planning in reducing the economic and social losses due to climate change effects.