The mountains of Himachal Pradesh are susceptible to landslides after heavy rain. The Kinnaur district is one of the areas in the state where landslides and other disasters are most likely to occur. This study indicates an adequate strategy for mapping the likelihood of landslides on steep terrain, especially for places without sufficient input data. The current project aimed to evaluate regulating factors of landslides and map the risk areas of Kinnaur using GIS and decision methods. Different influencing variables are considered, including relative relief, curvature, slope, type of soil, lithology, land use, drainage density, road distance, and rainfall. A landslide inventory was created using the data source of NASA from the 2010–2020 timeframe. A training set (80%) and a test set (20%) were created from a total of 113 landslide locations. A landslip susceptibility map for the research area was produced using the weighted linear combination (WLC) and the analytical hierarchy process (AHP). The weights of each variable and each variable class were determined using the AHP approach based on specialized knowledge of their relative relevance. The Landslide Susceptibility Index (LSI) was calculated using the WLC technique, which applied defined weights to all variable maps in raster format, and the region is separated into five vulnerability zones from very high to very low. This shows an appropriate method of prediction due to the ability to incorporate technical expertise while weighing the input parameters. The area under the curve (AUC) method was used to validate the analysis’ conclusions, and it suggests a significant vulnerability map. A success rate accuracy of 82.7% and a prediction rate curve accuracy of 81.4% were achieved.