Soil quality plays a critical role in agricultural productivity, environmental resilience, and sustainable land use, particularly in the dryland regions of northern Nigeria where soil degradation, low rainfall, and nutrient depletion threaten food security. This study employed a modified fuzzy logic model to assess and map soil quality across selected dryland areas using geospatial techniques. Ten key soil parameters were considered: nitrogen (N), phosphorus (P), potassium (K), organic carbon (OC), bulk density (BD), pH, soil depth, texture, drainage, and slope. These variables were standardized into fuzzy membership classes (low = 0, medium = 0.5, high = 1) based on agronomic thresholds and expert knowledge, and each was assigned a weight reflecting its importance in dryland soil productivity. The integration of these factors was conducted using overlay analysis within a Geographic Information System (GIS) environment, producing a composite soil quality index map. The results showed significant spatial variability in soil quality, with approximately 30.3% of the area classified as very high, 37.9% as medium, and 30.5% as low quality. High-quality soils were largely located in areas of dense vegetation such as parts of Kaduna, Niger, and northeastern Katsina, while low quality were found in regions experiencing leaching, shallow depths, and prolonged cultivation without adequate management. It provides a decision-support tool for sustainable land use planning, soil management, and policy interventions in dryland environments.