Landslides are the indicator of slope instability particularly in mountain terrain and causing different types of reimbursements and threats of life and property. The Himalayan terrains are highly susceptible to different natural hazards as well as disasters particularly land failure activities mainly due to inherent tectonic activities which further enhanced by various Neo-tectonic and Neolithic activities. This scientific study provides an enhanced framework for the assessment of proper and precise landslide susceptibility in the two districts of Arunachal Pradesh (Tawang and West Kameng) considering both physical and anthropogenic factors and various machine learning models (SVM, AdaBoost and XGBoost). At first, landslide inventory maps were developed based on previous landslide events. Here, 70% of the data were randomly selected for training and remaining was used for validation and optimization of the models using statistical implications and validation assessment methods. The result showed that the high and very high landslide susceptible areas are mainly concentrated in the middle portion along the Bhalukpong-Bomdia road section. Based on the AUC value and other statistical indicators it has been observed that AdaBoost is the most efficient model here (AUC = 0.92). AUC values of SVM and XGBoost are 0.85 and 0.89 respectively. AdaBoost model identifies that very low susceptibility class occupies 60.22% area and very high landslide susceptibility class occupies 15.51% area and it will be considered as more encouraging method for landslide susceptibility determination in this kind of cases for better accurateness. This high accuracy susceptibility map positively helps during the execution of various developmental projects.