Landslide hazards are responsible for causing substantial destruction and losses in mountainous region. In order to lessen the damage in these vulnerable areas, the key challenge is to predict the landslide events with accuracy and precision. The principal objective of the study conducted is to assess the landslide susceptibility along the transport corridor from Kullu to Rohtang Pass in Himachal Pradesh, India. To achieve this objective, a detailed landslide inventory has been prepared based on the imagery data and frequent field visits. A total of 197 landslides were taken under consideration including 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. Later, information gain ratio measure was used to identify the triggering factors having best score for eliminating the unimportant factors. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proved that the decision tree classification model of machine learning performed well and have a good accuracy in forecasting landslide susceptibility in the area considered for this study.