This study develops a district-scale landslide susceptibility map for Kohima, Nagaland, using the Frequency Ratio (FR) model within a GIS environment. A landslide inventory compiled from published sources, satellite interpretation, and limited field checks was randomly split into training (70%) and validation (30%) sets. Ten causative factors were prepared from DEM and thematic datasets: slope, aspect, elevation, land-use/land-cover (LULC), geomorphology, rainfall, and drainage density. FR values were computed for each factor class, and a pixel-wise Landslide Susceptibility Index (LSI) was obtained by summing the class FRs. The LSI raster was classified into five zones—Very Low, Low, Moderate, High, and Very High using natural breaks. Validation against the independent set shows an Area Under the ROC Curve (AUC) of 0.61, indicating good predictive skill. Spatially, susceptibility concentrates along major highway corridors, steep dissected hillslopes, weathered flyash formations, and rapidly urbanizing fringes around Kohima town. Approximately ~21% of the district falls within the High–Very High classes, highlighting priority tracts for slope stabilization, drainage management, and development control. The FR approach provides an interpretable baseline for hazard screening; future work can integrate time-varying rainfall thresholds and ensemble learning to refine predictions and quantify uncertainty for operational planning.