<p>The Himalayan region is characterized by active tectonics, frequent earthquakes, and mountain disasters, posing a serious threat to local residents. The long-term history of tectonic uplift plays a significant role in regional landslides distribution; however, this indicator is rarely used for landslide susceptibility assessments. This study integrates channel steepness index, which reflects tectonic uplift, as one of the conditioning factors along with seven conventional factors to assess landslide susceptibility in the Trishuli River watershed. This region, severely impacted by the 2015 Gorkha earthquake (M_w 7.8), is also of strategic importance as a proposed route for the Sino-Nepal railway. The MaxEnt model, recognized for its good interpretability, was used along with the Logistic Regression model for analysis. Two scenarios were developed to explore the effects of tectonic uplift: one including the steepness index and another excluding it. Results indicated that incorporating the steepness index enhanced model performance, as reflected by higher Area Under the Receiver Operating Characteristic values and other validation metrics. Contribution analysis using the MaxEnt model revealed tectonic uplift as the second most influential factor, contributing around 28% to the model’s predictive capacity, surpassing elevation and slope. Areas with steepness index above 50 and slopes steeper than 20° are found to be more susceptible to landslides. Additionally, the MaxEnt model outperformed Logistic Regression model. These findings underscored the contribution of tectonic uplift in landslide susceptibility assessments in mountainous areas. These insights contribute to improving disaster risk management and developing strategies to mitigate earthquake-induced landslides in the Himalayan regions.</p>