<p>Modeling landslide susceptibility in earthquake-prone regions is challenging due to the spatial variability and uncertainty of ground shaking. Robust characterization of ground motion is critical for preparing reliable susceptibility models. This study investigates the influence of large earthquake recurrence and associated ground motions on the spatial distribution of landslide probability in the Rasuwa district of central Nepal by integrating probabilistic seismic hazard assessment (PSHA) and landslide susceptibility modeling (LSM). Seismic hazard maps of peak ground acceleration (PGA) are generated for probabilities of exceedance of 50%, 10%, 5%, and 2% in a 50-year return period. A regional LSM is then developed using a coseismic landslide inventory from the 2015 M_w 7.8 Gorkha earthquake, combined with geo-environmental factors and PGA values from the USGS ShakeMap within a logistic regression framework. Scenario-dependent PGA variations are subsequently incorporated into the LSM to produce PSHA-based susceptibility maps. Results show that landslide susceptibility increases proportionally with PGA, with high to very high susceptibility zones concentrated in the southern part of the district, where ground shaking intensities are the greatest. Relative relief, slope, geology (e.g., phyllite and gneiss), and proximity to faults emerge as the dominant predisposing factors of landslides. Validation using the area under the receiver operating characteristic curve and overall accuracy demonstrates strong agreement (~89% and ~91%) with observed landslides. These findings highlight the reliability of the models in capturing hillslope responses under varying seismic scenarios and underscore their potential to support land-use planning, hillslope management, and disaster risk reduction in tectonically active regions such as the Nepal Himalaya.</p>