This study delineates landslide susceptibility zones in the Kalimpong district by integrating multi‐sensor datasets and assessing the effectiveness of statistical and machine learning models for precision mapping. The analysis utilises a comprehensive geospatial dataset, including remote sensing imagery, topographical, geological, and climatic factors. Four models were employed to generate landslide susceptibility maps (LSMs) using 16 influencing factors: two bivariate statistical models, frequency ratio (FR) and evidence belief function (EBF) and two machine learning models, random forest (RF) and support vector machine (SVM). Out of 1244 recorded landslide events, 871 events (70%) were used for training the models, and 373 events (30%) for validation. The distribution of susceptibility classes predicted by The RF and SVM models produced similar susceptibility distributions, predicting 13.30% and 14.30% of the area as highly susceptible, and 2.42% and 2.82% as very highly susceptible, respectively. In contrast, the FR model estimated 20.98% of the area as highly susceptible and 4.30% as very highly susceptible, whereas the EBF model predicted 17.42% and 5.89% for these categories, respectively. Model validation using receiver operating characteristic (ROC) curves revealed that the machine learning models (RF and SVM) had superior prediction accuracy with AUC values of 95.90% and 86.60%, respectively, compared to the statistical models (FR and EBF), which achieved AUC values of 74.30% and 76.80%. The findings indicate that Kalimpong‐I is most vulnerable, with 6.76% of its area categorised as very high susceptibility and 24.80% as high susceptibility. Conversely, the Gorubathan block exhibited the least susceptible, with 0.95% and 6.48% of its area classified as very high and high susceptibility, respectively. This research provides essential insights for decision‐makers and policy planners in landslide‐prone regions and can be instrumental in developing early warning systems, which are vital for enhancing community safety through timely evacuations and preparedness measures.
