Accurate forecasting of reference evapotranspiration (ETo) is crucial for sustainable water resource management and precision agriculture. The present study evaluates three ETo prediction methods: Random Forest (RF), Cartesian Genetic Programming (CGP), and Convolutional Neural Network-Graphics Processing Unit (CNN-GPU) across time intervals of 1 to 364 days. Using dispersion analysis (scatter/violin plots) and accuracy metrics (RMSE, MAE, R^2, SI), it was seen that the RF and CNN-GPU models consistently outperform CGP, particularly at extended horizons. At 364 days, CNN-GPU achieved the highest accuracy (RMSE: 0.678 mm/day, R^2: 0.874), while RF maintained robust performance (RMSE: 0.683 mm/day, R^2: 0.872) and minimal dispersion (SI: 0.244--0.278). In contrast, CGP exhibited slightly higher error indices (RMSE: 0.702mm/day) and greater variability. Statistical validation via t-tests, F-tests, and ANOVA revealed significant differences in performance, especially at longer lags (p < 0.05), with CNN-GPU often showing superior accuracy. Time-series analyses further confirmed that RF and CNN-GPU effectively capture seasonal ETo trends, while CGP struggles with increased lag.