Landslides pose a severe environmental and socioeconomic threat in mountainous regions, yet existing spatial risk assessments often isolate hazard prediction from actual land-use exposure. This study develops an integrated geospatial framework to evaluate landslide susceptibility and quantify anthropogenic exposure in Tinggimoncong District, Indonesia, to support sustainable land management. A Random Forest algorithm was utilized within Google Earth Engine to model landslide susceptibility using an inventory of 198 landslide and non-landslide points and 11 parameter predictors. Concurrently, a land-use classification was performed using Sentinel-2A imagery. The susceptibility model achieved robust predictive performance with an Area Under the Curve (AUC) of 0.843 and an Overall Accuracy of 80.3%, identifying slope gradient and distance to roads as the most dominant landslide-driving factors. The validated land-use map with Overall Accuracy of 85.6% was spatially intersected with high-susceptibility zones (>0.7), revealing that 16.97% of the road infrastructure and 4.90% of settlements are highly exposed to landslide hazards, contrasting with a minimal exposure 0.22% for agricultural lands. These findings provide a robust scientific foundation for sustainable land management, recommending targeted interventions such as a moratorium on new construction in red zones, structural geotechnical engineering on vulnerable road-cut slopes, and deep-rooted vegetative conservation on exposed agricultural lands.