Countries with arid and semi-arid climates face the dual challenge of water scarcity and increasing food demand. One of the key parameters for addressing this challenge is reference evapotranspiration (RET). Numerous methods, ranging from empirical equations to machine learning algorithms, have been developed for RET estimation. However, these methods often require considerable expertise and access to meteorological data, limiting their widespread application and reducing the adoption of up-to-date approaches in water resources management. Therefore, this study developed AgriAide, a software tool capable of generating long-term daily RET data for regions lacking meteorological stations or complete weather records without requiring any user-provided input data. The software calculates RET based on the FAO-56 Penman–Monteith equation using the ERA5 reanalysis dataset for a 20-year period and provides the results through a simple and user-friendly interface. To evaluate its performance, RET estimates were compared with station-based calculations from 16 meteorological stations using standard statistical indicators. The results showed excellent agreement between the software outputs and the reference values, with coefficients of determination (R²) greater than 0.90 and normalized root mean square error (nRMSE) values below 20% for most stations. These findings demonstrate that AgriAide provides reliable RET estimates and can be confidently applied in regions where meteorological stations are unavailable or where weather records are incomplete for applications such as irrigation management, hydrological studies, drought monitoring, and water resources management.