<p>The Garhwal Himalaya is highly prone to rainfall-induced landslides that disrupt connectivity, endanger lives, and weaken socioeconomic resilience of the region. Rapid urbanization and infrastructure expansion in this fragile terrain have further exacerbated slope instability. This study presents a subdistrict-level landslide susceptibility assessment for 58 subdistricts in the Garhwal region using a multi-criteria decision-making (MCDM) framework. Nine thematic layers used as Landslide Susceptibility Index (LSI) determinants, including slope, aspect, elevation, geology, land use/land cover, soil erosion susceptibility, monsoon rainfall, proximity to rivers, and proximity to roads, were derived using remote sensing datasets and GIS-based analyses. The Analytical Hierarchy Process (AHP) was applied to assign criteria weights. The slope, monsoon rainfall, and geology were identified as the most influential factors, followed by proximity to roads, land use/land cover, proximity to rivers, soil erosion susceptibility, elevation, and aspect. The composite LSI classified subdistricts into five categories: very low to very high. The assessment classified 55% of the subdistricts, particularly in Chamoli, Rudraprayag, and Uttarkashi, as high to very-high susceptibility zones. Validation against 3,484 historical landslide points yielded an Area Under the Curve (AUC) of 0.768, indicating fair predictive performance of the susceptibility model. These findings provide a critical spatial framework for district-level authorities to prioritize disaster mitigation resources and enforce land-use regulations, contributing to the overall climate resilience of the region.</p> Graphical Abstract <p></p> <p>This work represents the first effort to develop a comprehensive sub-district scale landslide susceptibility assessment for the Garhwal Himalaya region, supporting decision-makers in addressing the area’s critical challenges related to rainfall-induced slope failures and natural hazards. In this study, we utilized an integrated Multi-Criteria Decision-Making framework combining remote sensing datasets, GIS-based spatial analysis, and the Analytical Hierarchy Process to systematically evaluate nine key causative factors including slope characteristics, geological formations, monsoon rainfall patterns, land use dynamics and anthropogenic influences such as road networks and urbanization. We proposed a robust and replicable methodological approach to map the spatial distribution, intensity, and frequency patterns of landslide susceptibility across 58 subdistricts in this seismically active and ecologically fragile mountainous terrain. Ultimately, this study identifies critical high-susceptibility zones where 55% of subdistricts, particularly in Chamoli, Rudraprayag, and Uttarkashi, exhibit high to very-high susceptibility, providing essential spatial intelligence for targeted disaster mitigation planning, land-use regulation enforcement, resource allocation optimization, and long-term climate adaptation strategies in the vulnerable Himalayan region.</p>