The water scarcity crisis in the agricultural sector, particularly in rice cultivation, necessitates the optimization of irrigation management. This study aimed to evaluate and conduct a long-term simulation of the effects of various irrigation management strategies on the grain and biological yields of a local rice cultivar (Hashemi) using the CERES-Rice model at the Rasht Rice Research Station. Meteorological, soil, and agronomic data spanning a 18-year period (2004 to 2021) were utilized. The calibration of genetic coefficients was performed using data from the continuous flooding treatment between 2004 and 2016, while independent model validation was carried out using data from 2017 to 2022. Results indicated that the model demonstrated “excellent” accuracy in simulating grain yield; the nRMSE index was 8.06% in the calibration phase and decreased to 7.49% during the validation phase. Additionally, the model’s accuracy in estimating biological yield was evaluated as “good” (with an nRMSE of approximately 13%). These results demonstrate that the CERES-Rice model provides reliable and accurate simulations of the growth and yield of the Hashemi rice cultivar under different irrigation management practices. Therefore, the model can serve as an effective decision-support tool for long-term simulation studies, evaluating alternative irrigation management scenarios, predicting crop responses to different water management strategies, and reducing the need for costly and time-consuming field experiments. Its application can facilitate evidence-based decision-making, improve irrigation planning, optimize water use, and support the development of sustainable rice production systems under increasing water scarcity and climate variability.