Kashmir Himalaya have witnessed numerous mass wasting events in the last few decades leading to the degeneration of its paleoenvironment. The frequency of these events has increased manifold due its intricate geology and the ongoing developmental work and other anthropogenic activities taking toll on the geo-environment of the valley. This work presents comparative performance of three statistical models in geographic information system (GIS) environment for developing landslide susceptible map (LSM) in and around the Gool area of Ramban district in Jammu and Kashmir union territory, India. The total of 365 landslides were spotted in designated area masking 0.909 km2 of study area. 11 causative factors were evaluated for generating LSM which include slope, aspect, curvature, drainage density, relative relief, distance to fault, distance to road, stream power index, land use land cover, topographic wetness index and lithology. Both the remote sensing data and ancillary datasets has been utilized to compile spatial database. Statistical models like frequency ratio (FR), information value (Inv) and analytical hierarchy process (AHP) are utilized to develop LSM for the area. The sensitivity analysis of the receiver operator curve (ROC) are used to estimate the accuracy of the models. The ROC validated results revealed an accuracy of 0.80, 0.80 and 0.74 for Inv, FR and AHP respectively. The accuracy and efficiency findings suggest that all models can provide a credible map of landslide susceptibility for the study region; however, the FR and Inv models are somewhat more accurate than the AHP model.