The northeastern Himalayan region of India experiences frequent landslide events owing to its complex topography, geological formations, and precipitation patterns. One of the landslide-prone areas in this region is the West Karbi Anglong district in Assam. This study represents a comprehensive approach to landslide susceptibility mappingLandslide susceptibility mapping by integrating Geographic Information System (GIS)Geographic Information System (GIS) techniques with Multivariate Binary Logistic RegressionBinary logistic regression (MBLR) modeling. Eleven causative factorsCausative factors were systematically analyzed across four categories: morphometric, geological, environmental, and triggering factors. A comprehensive landslide inventoryLandslide inventory comprising 77 verified landslide locations was developed using data from the Geological Survey of India and high-resolution LISS-IV satellite imagery spanning 2021–2023. The dataset was partitioned into 70% for model training and 30% for validation. The MBLR model identified drainage densityDrainage density (β = 2.515, p = 0.004) and slope (β = 0.205, p < 0.001) as the primary controlling factors, whereas vegetation cover (NDVINormalized Difference Vegetation Index (NDVI): β = − 26.631, p < 0.001) demonstrated significant protective effects. The resulting susceptibility map classified the study area into five zones: very low (1419 km2), low (599 km2), moderate (468 km2), high (360 km2), and very high (266 km2) susceptibility. Model validation using Receiver Operating Characteristic (ROC)Receiver Operating Characteristic Curve (ROC) analysis achieved an Area Under Curve (AUCArea Under the Curve (AUC)) value of 0.84, indicating good predictive accuracy. This research provides essential insights for land-use planning, infrastructure development, and disaster risk reductionDisaster risk reduction strategies in landslide-prone Himalayan regions, demonstrating the effectiveness of statistical modeling approaches for geological hazard assessment.