Accurate and efficient irrigation scheduling of forage maize plays a crucial role in sustainable water resource management, enhancing food security, and achieving self-sufficiency in the production of strategic crops. In this study, an innovative approach for optimizing irrigation scheduling was developed based on the simultaneous integration of weather forecasts with the AquaCrop model. Applying this approach throughout the growing season allowed optimization of irrigation at different phenological stages, ensuring that the minimum required irrigation depth was applied at each event without inducing water stress, thereby maximizing crop water productivity. Field experiments were conducted in summer 2024 on forage maize (SC704) under surface drip irrigation, using three irrigation scheduling treatments: 1) based on evapotranspiration calculations and the FAO-56 method with real-time meteorological data, 2) combining three-day weather forecast data with the AquaCrop model, and 3) following the conventional farmer practice in the region, each with three replications. The aim was to compare and evaluate the efficiency of the combined AquaCrop and weather forecast approach against traditional farmer practices and ET-based scheduling methods. Results showed that the treatment using the combined approach (treatment 2) saved 110 mm of irrigation water (equivalent to a 19% reduction in irrigation depth) compared to treatment 1. Moreover, it outperformed both ET-based (treatment 1) and conventional farmer practices (treatment 3) in producing fresh and dry biomass, achieving the highest water productivity of 14.48 and 4.88 kg m⁻³ for fresh and dry biomass, respectively. Overall, the findings highlight the high potential of this approach for developing smart irrigation systems, automating irrigation operations, and improving water management scheduling at the field scale.