Regions with intricate topography and dynamic climatic conditions face significant landslide hazards. Landslide susceptibility analysis employs various methods, including heuristic methods, machine learning, deterministic models, hybrid methods, probabilistic methods, geospatial techniques, and statistical methods like the Frequency Ratio (FR) method. The FR method is preferred for its simplicity, efficiency, and ability to quantify relationships between landslide occurrences and causative factors, making it ideal for regions with limited data. This study focuses on mapping and analyzing landslide susceptibility in the Amboori region in Kerala State by integrating Geographic Information Systems (GIS) with the FR model. Amboori, prone to devastating landslides due to steep gradients, monsoon rains, and human activities, is ideal for such analysis. Advanced GIS tools like ArcGIS and QGIS were used to process datasets, including (Digital Elevation Model) DEM, slope, curvature, drainage, precipitation, Land Use Land Cover (LULC), lithology, Normalized Difference Vegetation Index (NDVI) and soil maps. The FR model quantified relationships with historical landslides and the methodology involved systematic data preprocessing, thematic map building, and FR value computation. Model reliability was validated using Success Rate Curve (SRC) and Area Under the Curve (AUC) evaluation. Landslide Susceptibility maps are developed for the study area dividing the region into low, medium, and high-risk zones. The developed maps can guide local governing bodies in disaster risk reduction and sustainable land-use planning. Policymakers, engineers, and urban planners can utilize these maps to develop effective mitigation strategies, enhancing community resilience against landslide hazards.