Soil quality assessment is crucial for sustainable land management. Given the high cost and time required to measure all soil quality indicators, minimum data set (MDS) selection offers an efficient approach for accurate evaluation. This study identifies an optimal MDS and examines its spatial distribution in the Mashhad Plain. A total of 180 soil samples (0-10 cm depth) were analyzed for physical and chemical properties. The soil quality index (SQI) was computedusing the weighted additive integrated quality index (IQIw) in four scenarios: total dataset-linear (IQIwL_TDS), total dataset-nonlinear (IQIwNL_TDS), minimum dataset-linear (IQIwL_MDS), and minimum dataset-nonlinear (IQIwNL_MDS). Among 11 physical and chemical properties, principal component analysis (PCA) identified sand, electrical conductivity (EC), pH, soilorganic carbon (SOC), calcium carbonate equivalent (CCE), and nickel (Ni) as the MDS. IQIwL_MDS yielded the highest SQI, while IQIwNL_MDS produced the lowest. The nonlinear model (R² = 0.89) showed a stronger correlation between MDS and TDS than the linear model (R² = 0.76), underscoring the nonlinear model’s predictive accuracy. Global Moran’s I revealed a clustered spatial pattern, while Getis-Ord Gi* identified low-quality hotspots in the southern and southeastern regions, predominantly in barren lands. This study presents an innovative framework by integrating MDS selection and spatial analysis, offering a robust methodology for soil quality assessment in semi-arid regions. The findings provide valuable insights for sustainable soil management and conservation strategies in vulnerable landscapes.

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