Study region: Iran, characterized by diverse climatic conditions, including arid, semi-arid, and humid subtropical regions, where ET0 dynamics vary significantly due to climatic differences. Study focus: Reference evapotranspiration (ET0) is a fundamental component of hydrological modelling and plays a critical role in agricultural water management. Reliable ET0 predictions are essential for optimizing irrigation systems and estimating water demand. This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET0 across these diverse climatic conditions. Daily ET0 values were calculated using the FAO-56 Penman-Monteith model and validated against groundbased observations from 50 weather stations (2008-2017). The RF model was trained using ERA5-Land climatic variables (air temperature, relative humidity, and ET0 from ERA5-Land) along with the day of the year (DOY). New hydrological insights for the region: Results demonstrated a high correlation between ERA5Land temperature estimates and observed station data (Pearson correlation coefficient, r = 0.97; Root Mean Square Error, RMSE = 2.77 degrees C), while relative humidity showed a weaker agreement (Normalized Root Mean Square Error, NRMSE = 21 %). The RF model outperformed traditional approaches in arid and semi-arid regions, achieving NRMSE values of 25 % and 28 %, respectively, with a 60 % improvement over humid regions. At the 8-day scale, predictive accuracy improved further (RMSE = 6.05 mm/8 days, r = 0.99). Beyond model performance, this study provides new insights into the spatiotemporal variability of ET0 across different climatic zones. The findings indicate that temperature is the dominant climatic factor driving ET0 variability, while relative humidity exhibits higher uncertainty, particularly in humid regions. Seasonal trends highlight notable summer ET0 peaks exceeding 30 mm/day in arid zones, emphasizing the need for climate-adaptive irrigation strategies. The proposed methodology is computationally efficient, requiring minimal input variables, and demonstrates robust and scalable performance for large-scale ET0 estimation. These findings provide a cost-effective solution for water resource management, drought monitoring, and climate change adaptation, particularly in data-scarce regions.