Wheat productivity is often constrained by suboptimal sowing times and inappropriate cultivar selection, leading to reduced yields under varying environmental conditions. Identifying the best sowing dates and suitable cultivars can enhance growth performance and yield outcomes. A field experiment was conducted at the Agronomic Research Area, University of Agriculture, Faisalabad, to evaluate the effects of four sowing dates (November 5ᵗʰ, November 20ᵗʰ, December 5ᵗʰ, and December 20ᵗʰ) and four wheat cultivars (Arooj-22, Subhani-21, Nawab-21, and Sadiq-21) on wheat growth and yield. The experiment followed a Randomized Complete Block Design (RCBD) with split arrangements and three replications. Results indicated that early sowing, particularly on November 20ᵗʰ, led to the highest grain yield (4932.25 kg ha⁻¹) and a maximum number of productive tillers (373 m²). Among cultivars, Arooj-22 and Subhani-21 exhibited superior performance, with Arooj-22 yielding 4574.26 kg ha⁻¹ and Subhani-21 achieving the highest Leaf Area Index (4.05). The DSSAT model was calibrated and evaluated using the field data, showing strong agreement between observed and simulated values. Model calibration produced low root mean square error (RMSE) values for key parameters, including days to anthesis (RMSE = 1-2), LAI (RMSE = 0.4-0.7), and grain yield (RMSE = 2-85). Model evaluation across different treatments yielded RMSE values of 314.14 to 673.18 for grain yield and 0.22 to 1.89 for LAI, indicating acceptable prediction accuracy. The rise in temperature significantly impacts wheat yield, with varying effects across different varieties. Arooj22 saw a 1.20% yield decrease at 2.5°C, rising to 26.28% at 4.5°C. Subhani-21's yield dropped by 8.32% at 2.5°C and 30.86% at 4.5°C. Nawab-21 was the most affected, with a 16.33% yield reduction at 2.5°C and 38.84% at 4.5°C, while Sadiq-21 and Arooj-22 showed greater resilience. These results highlight the importance of optimal sowing time and cultivar selection, as well as the reliability of the DSSAT model for simulating wheat growth under varying environmental conditions.