<p>Landslides are geomorphological hazards involving the downward movement of rock, debris, or soil along slopes, often triggered by rainfall, seismic activity, and human interventions. India is one of the most landslide-prone countries in the world, particularly in tropical regions where high-intensity monsoon rainfall, complex terrain, and fragile geological conditions significantly reduce slope stability. Despite frequent landslide occurrences, there is a research gap in the systematic evaluation and comparison of reliable statistical models for landslide susceptibility mapping in such tropical mountainous environments. The primary objective of this study is to assess and compare the effectiveness of the Frequency Ratio (FR) and Evidential Belief Function (EBF) models for landslide susceptibility mapping using a GIS-based framework. The study was conducted in Coorg District, Karnataka, India, a high-rainfall region of the Western Ghats characterized by steep slopes and recurrent landslide events. Landslide susceptibility maps were classified into five zones ranging from very low to very high susceptibility. Model validation using the Area Under the Curve (AUC) method demonstrated high predictive accuracy, with success rates of 87.6% for the FR model and 87.2% for the EBF model. The results confirm that both models are effective in delineating landslide-prone areas, and this analysis helps understand the region’s landslide vulnerability and supports the adoption of suitable mitigative measures in the future, thereby supporting informed land use planning and disaster risk reduction in tropical mountainous regions.</p>