This study presents a practical geospatial approach, based on geomatic principles, to create landslide susceptibility maps that meet contemporary landscape and land management priorities. By employing a GIS-based statistical modeling, our methodology seamlessly integrates a wide range of factors including topography, lithology, land use, and precipitation. This comprehensive approach allows for a holistic evaluation of landslide susceptibility. We use two widely recognized multi-criteria techniques, namely the Analytic Hierarchy Process (AHP) and the Fuzzy Logic Ratio (FR), which in result produce two distinct yet complementary landslide susceptibility maps (LSMs). The creation of these LSMs relies on a carefully curated dataset of landslides, collected through rigorous analysis of high-resolution satellite imagery, interpretation of aerial photographs, and extensive fieldwork. Eleven key factors are selected to inform the modeling process. To assess the accuracy of the LSMs, we employ ROC curves, with the FR method demonstrating superior predictive performance, achieving an impressive accuracy rate of 75% compared to the AHP model's 65%. These findings highlight the effectiveness of our approach in identifying high landslide susceptibility areas, providing valuable insights for informed land use planning, hazard mitigation strategies, and rapid emergency response measures. The GIS-based statistical modeling technique showcased in this research provides a robust framework for generating precise landslide susceptibility maps in complex mountainous landscapes. This research makes a significant contribution to the evolving field of geomatics, enhancing landscape resilience and promoting sustainable land management practices.