Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when screening large numbers of soil-climate-management combinations. The present study addresses this constraint by developing and evaluating five machine learning (ML) surrogate models-Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine for regression (SMOreg), RandomTree, and Reduced Error Pruning Tree (REPTree)-trained to emulate the AquaCrop-GIS response surface for durum wheat (Triticum durum Desf.) grain yield across the Capitanata plain (Southern Italy). A dataset of 342 instances was constructed by crossing 25 soil profiles, three sowing dates, and two irrigation regimes across 15 climatic grid cells (2014-2023), evaluated by stratified 10-fold cross-validation. The MLP achieved the highest accuracy (R = 0.983; R2 = 0.966; RMSE = 0.083 t ha-1); the four interpretable models were clustered at R = 0.891-0.907 (RMSE = 0.192-0.203 t ha-1). All models converged on consistent agronomic signals: standard sowing (1 November) yielded +0.53 t ha-1 over late sowing (15 November), supplemental irrigation added +0.17 t ha-1, and fine-textured soils produced superior yields. The convergence of directional signals across linear, kernel-based, and tree-based architectures confirms that ML surrogates trained on process-model outputs can efficiently emulate AquaCrop response surfaces and deliver actionable management guidance for durum wheat producers and agricultural planners in Mediterranean dryland farming systems.