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Natural Disasters like landslides affect livelihood and nature. To mitigate this hazard, scientists developed Landslide Susceptibility Mapping (LSM), which helps to identify landslide-prone zones. With the advancements in Geographical Information Systems, machine learning approaches have taken over heuristic techniques for LSM. However, model uncertainty has yet to be considered. This study focused to use the advantage the uncertainty analysis to generate more precise LSM. The present study considered twenty-one geo-environmental factors to evaluate LSM in the Darjeeling Himalayas. 1,888 landslide locations were used to prepare the landslide inventory, and 1,888 non-landslide points were carefully created for model training purposes. Seven advanced machine learning methods, viz., naive Bayes, boosted decision tree, linear discriminant analysis, flexible discriminant analysis, monotone multilayer perceptron, gradient boosting machine, and extreme gradient boosting, were utilized for preparing landslide susceptibility maps. The constructed maps were then categorized into five susceptibility classes, viz., very low, low, moderate, high, and very high, and these were validated through the Area Under Receiver Operating Characteristics curve, Kolmogorov–Smirnov statistics, and Quality Sum method. The machine learning model's performance was evaluated through classification metrics, viz., overall accuracy, sensitivity (recall), specificity, precision, and F1-score. With AUCROC values greater than 0.90 for both the training and testing datasets, KS statistics values of 94.6 and 74.5, respectively, and Quality sum index of 2.671 and 2.058, respectively, XGBoost and GBM were found to be better performing than the rest of the utilized models. An uncertainty analysis was attempted using the coefficient-of-variation method and aleatoric uncertainty (lowest value of 0.024 for XGBoost and highest value of 0.25 for LDA). A confidence map for each susceptibility map was generated, which can be utilized as a reference for policymakers to formulate landslide mitigation strategies on a regional scale.