<p>Landslides are a significant hazard to Indian mountainous regions, causing loss of life and damaging property. In Meghalaya, landslides are the most significant disasters because of climate change, deforestation, uncontrolled urbanization, and improper land use and planning. Rainfall-induced landslides are the most common in East Khasi Hills (EKH), a regional district of Meghalaya. Therefore, the development of cities in hilly areas has led to extensive studies and research. Landslide Susceptibility Map (LSM) is essential to help prevent damage and save lives. This study aims to develop an LSM for the EKH District of Meghalaya, India, considering nine conditioning parameters that cause landslides, such as slope, aspect, curvature, Land Use Land Cover (LULC), rainfall, Distance to road (DTR), lithology, geomorphology, and elevation. This study uses the Frequency Ratio (FR) and Statistical Index (SI) methods to analyze the relationship between the selected factors and past landslides and is used to develop LSM. The study results reveal that the FR method identified 16.5% of the area as high landslide risk, while the SI identified 23.3%. SI classification provides a broader safety margin, while FR classification provides more precise high-susceptibility areas. Finally, developed maps are validated with the Area Under the Curve (AUC) and value of the Receiver Operating Characteristics (ROC) curve. The validation results showed that FR has a higher AUC value of 0.927 than that of the SI method at 0.828. Validation results showed that the FR model predicts landslides better than the SI method. Landslides are more prevalent in built-up areas near roads and on south-facing slopes. The produced maps can be beneficial for planning to deal with disasters, determine the best use of the land in different areas, and find ways to prevent landslides from causing damage.</p>