<p>This study assesses landslide susceptibility in the Rudraprayag to Badrinath region of the North Western Himalayas, Uttarakhand, India, using spatial statistics and machine learning techniques. A dataset of 268 landslide and 268 non-landslide points was analyzed to explore the spatial distribution of landslides. To achieve this, advanced spatial analysis methods including Average Nearest Neighbor (ANN), Moran’s I, K Function Analysis, and Getis-Ord Gi* Hotspot Analysis were integrated with Random Forest modeling. This integration aims to improve landslide susceptibility mapping. As a result of these analyses, the study found notable clustering of landslides impacted by environmental and geomorphological elements such as slope, elevation, seismic activity, and river proximity. Furthermore, cold spots were identified in more stable locations, while hotspot analysis highlighted high-risk areas, mostly in regions with steep slopes, weak lithology, and proximity to river systems. The Random Forest model further revealed elevation, slope, seismic activity, urbanization, and rainfall as key contributing factors in landslide susceptibility. Based on these findings, the study highlights the necessity of focused mitigation methods in high landslide prone locations and clearly identifies zones of varied susceptibility. In summary, the findings provide valuable insights into landslide dynamics and contribute to more effective landslide risk management in the region.</p>