Abstract: The Hetao Plain is one of the most important grain-producing areas in China. But several challenges are still remained in the region, due to the lack of adequate data. Existing research has been focused mainly on field-scale studies. It is difficult on the large-scale simulations at the regional level. In this study, the parameters of the SWAP (soil-water-atmosphere-plant) model were optimized in the entire Hetao Plain. The spring maize, spring wheat, and sunflower were taken as the research objects. Some indicators were also determined, including soil moisture, salinity, leaf area index (LAI), and yield. These datasets were collected from seven test sites over 21 site years. The parameters were then calibrated with the PEST (Parameter ESTimation) algorithm. Additionally, the proximity and meteorological data were selected from the nearest national weather stations. The SWAP model was then verified in the corresponding sites. The SWAP model was also optimized to improve the simulation accuracy of the key agronomic and environmental variables. The optimal parameters have significantly enhanced the accuracy of soil water and salinity in the SWAP model. Particularly, the soil water and salinity dynamics were represented during crop growth. In spring maize and spring wheat, the coefficient of determination (R²) values for the soil moisture simulations reached 0.77 and 0.58, respectively, while the R² values for the soil salinity simulations were improved from negative values to above 0.3. In Sunflower, the simulation of LAI was notably enhanced with an R² value of 0.99, although the optimization slightly improved the accuracy of water and salinity process simulations. Optimal parameters were achieved in the significant improvements of the yield simulation; The R² values increased from negative values to 0.89 for spring wheat, 0.34 for spring maize, and 0.96 for sunflower, compared with the default parameters. Furthermore, 23 key parameters were identified after sensitivity analysis. The high accuracy of the simulation was attributed to the optimal parameters responsible for the soil hydraulic properties and crop-specific factors. Therefore, the maize and sunflower (C4 crops) were more efficient in the CO₂ assimilation and water use, compared with the spring wheat (C3 crop). The optimal SALTMAX (the soil salinity threshold at which crop water uptake was affected) values were aligned with the salt tolerance of crops. Spring wheat was better suited to the cooler planting seasons (March), while maize and sunflower were required the higher temperatures for growth (May–September). The higher Q10 value of spring wheat indicated a greater sensitivity to temperature during respiration. A combination of optimal parameters was obtained for the SWAP model, specifically tailored for the Hetao Plain. The performance was significantly enhanced to simulate the complex hydrological and agronomic processes. The findings can provide a valuable reference for agricultural hydrological modeling in similar arid environments. A great contribution was also gained for the more sustainable water and soil management in the regions with water scarcity.