Quantitative land suitability assessment for dryland crops in semi-arid regions requires methods with acceptable accuracy in yield prediction. In this study, the capability of two approaches, AEZ and the AquaCrop model, was compared for predicting dryland wheat yield (cv. Azar 2) in the Halab region (Zanjan province). Climatic data from two growing seasons (2022–2023 and 2023–2024), soil properties of 11 profiles, and actual wheat yield were measured. Attainable yield using the AEZ method was estimated through calculation of potential yield, application of water stress, and the edaphic index. The AquaCrop model was also calibrated and executed. Accuracy evaluation was performed using MAE, RMSE, nRMSE, r², and R² indices. The results showed that the AquaCrop model (MAE=0.155, RMSE=0.189 t/ha, nRMSE=10.57%, r²=0.777, and R²=0.657) had higher accuracy compared to the AEZ method (MAE=0.178, RMSE=0.235 t/ha, nRMSE=13.17%, r²=0.728, and R²=0.544). The superiority of AquaCrop is due to its dynamic daily simulation of water stress with separation of evaporation from transpiration. Land suitability classification showed that although estimates differed between the two models, all land units fell into class S2, and the predicted yield of all units was above the critical yield (412 kg/ha), indicating the economic justification of cultivation in the region. The AquaCrop model is more suitable for field-scale yield prediction and detailed land suitability assessment under dryland conditions, while the AEZ has greater applicability for regional macro-planning. The development of hybrid models that integrate dynamic water stress simulation with nutrient uptake sub-models is recommended to increase prediction accuracy in future studies.