Soil quality is fundamental to agricultural sustainability, optimal land management, and productivity. This study determined a minimum data set (MDS) for evaluating soil quality and compared three indices additive (SQIa), weighted (SQIw), and Nemoro (NQI) in dryland wheat areas of the Qorveh Plain. Soil samples (0–30 cm depth) were collected to measure physical, chemical, and fertility attributes. Using principal component analysis (PCA), six properties (sand %, silt %, cation exchange capacity, pH, organic carbon, and available potassium) were selected for the MDS. Indices were calculated using both the total data set (TDS) and MDS, and regressed against crop yield. Results revealed a significant correlation (p < 0.01) between wheat yield and all soil quality indices. Under the MDS framework, the highest coefficient of determination was achieved by SQIw (R2 = 0.75), followed by NQI (R2 = 0.67) and SQIa (R2 = 0.63). A matching pattern emerged for the TDS baseline, though with lower predictive values (R2 = 0.70, 0.61, and 0.51, respectively(. Consequently, the weighted additive index (SQIw) demonstrated a superior capacity to explain yield variations. Due to higher yield correlations, lower analytical costs, and a reduced indicator count, the MDS approach proved more efficient than using the full TDS. Ultimately, the overall soil quality of the agricultural land in the study region was classified as poor (Class IV).