<p>Earthquake-induced landslide susceptibility zonation (EQ-LSZ) mapping commonly relies on the landslide inventory. However, in many seismically active regions, the lack of comprehensive landslide inventories poses challenges for susceptibility mapping. This study focuses on the Indian Himalayan region, specifically Sikkim, which experienced a series of earthquake-induced landslides, including those triggered by the 2011 Sikkim earthquake. The research develops EQ-LSZ maps using both inventory-inclusive methods—statistical models (Frequency Ratio, FR) and machine learning models (Random Forest, RF)—and an inventory-exclusion method (Newmark Displacement, ND). The study also performs a comparative analysis of these models. Input data include landslide inventory, seismic parameters (peak ground acceleration), landslide-controlling factors (topography, lithology, distance to faults, hydrology, distance to roads, land use/land cover, geomorphology, soil properties), factor of safety, and yield acceleration. Model performance was evaluated using success and prediction rate curves. The FR method achieved 85.34% prediction accuracy and 84.38% success rate; the RF method reached 86.04% prediction accuracy and 84.43% success rate, while the ND method attained 70.00% prediction accuracy and 72.78% success rate. A change in susceptibility class assessed the interchangeability of inventory-based and non-inventory-based models. Results reveal a 90.07% area variation up to the second degree between the FR and ND methods and an 84.1% variation between the RF and ND methods, indicating minor susceptibility shifts. These findings suggest that non-inventory-based models, such as ND, can provide valuable alternatives for regions lacking earthquake-induced landslide inventories. The study offers critical insights for improving EQ-LSZ mapping in data-scarce, seismically active regions.</p>