Loss of life and property due to human induced and natural landslides are common in Himalayan region of Nepal. Intense precipitation over a short period of time, unplanned road construction, degradation of forests, and change in land use are expected to increase the number and intensity of landslides in the already fragile mountainous tracts of the country. The landslide susceptibility assessment using GIS and remote sensing tools identifying hazard/susceptibility, vulnerability and risk are very useful for disaster risk reduction and management. In this chapter, Statistical Index Model and Logistic Regression Model were compared for performance through Geographical Information System (GIS), to derive landslide Susceptibility map of the Chepe River corridor. Eleven factors (slope, aspect, geology, distance to road, land use, rainfall, elevation, relief, drainage density, plan curvature, and profile curvature) were considered as possible key factors for the landslide susceptibility assessment. To validate the models, Receiver Operating Characteristic (ROC) was used. The result shows the Logistic Regression Model has 82% prediction accuracy, whereas Statistical Index Model has 63% prediction accuracy. Based upon the accuracy assessment, the logistic regression model seems to have better applicability in the Chepe River corridor in comparison to statistical index method using same eleven triggering factors.