This work is a novel trial to integrate geostatistics with fuzzy logic under the geographic
information system (GIS) environment to model soil pollution. Soil samples from seventy-one soil
profiles in the northern Nile Delta, Egypt, and were analyzed for total concentrations of Cd, Co, Cu,
Pb, Ni, and Zn. Metal distribution maps were generated using ordinary kriging methods. They
were normalized by linear and non-linear fuzzy membership functions (FMFs) and overlain by fuzzy
operators (And, OR, Sum, Product, and Gamma). The final maps were validated using the area under
the curve (AUC) of the receiver operating characteristic (ROC). The best-fitted semivariogram models
were Gaussian for Cd, Pb, and Ni, circular for Co and Zn, and exponential for Cu. The ROC and AUC
analysis revealed that the non-linear FMFs were more effective than the linear functions for modeling
soil pollution. Overall, the highest AUC value (0.866; very good accuracy) resulted from applying
the fuzzy Sum overly to the non-linearly normalized layers, implying the superiority of this model
for decision-making in the studied area. Accordingly, 92% of the investigated soils were severely
polluted. Our study would increase insight into soil metal pollution on a regional scale, especially in
arid regions.