This study presents a data-driven methodology for predicting the pressure coefficient statistics on the windward wall, roof, and leeward wall of low-rise buildings situated downwind of complex heterogeneous terrains. Two types of artificial neural network models were developed: the empirical parameter-based ANN (PANN) and the morphology-based ANN (MANN). Pressure data from wind tunnel tests on the Wind Engineering Research Field Laboratory (WERFL) building model (building height H = 4 m) in complex heterogeneous terrain were used to develop the ANN models. These models were evaluated against a non-linear fitted model to assess their predictive performances. PANN and MANN demonstrated superior performance in capturing the effects of terrain complexity on the mean (Cp,mean) and the root-mean-square (Cp,RMS) wind pressure coefficients for the windward wall, roof, and leeward wall. Optimal prediction was achieved with a terrain patch size of W L=4 2, equating to a full-scale area of approximately 72 m 23 m. This suggests that the morphology within approximately 100 m 50 m (25H 12.5H) in front of a low-rise building has the greatest correlation with the wind pressure coefficient. Despite lower R2 values for max Cp,RMS on the leeward wall across all models, both PANN and MANN showed promising accuracy for the six outputs studied. Moreover, a global sensitivity analysis confirmed the impact of terrain roughness and complexity on the prediction models particularly on max Cp,RMS, and underscored the dominance of effective roughness length and the coefficient of variation of roughness length in influencing model outcomes.