Accurate estimation of daily reference evapotranspiration (ETo) is essential for effective irrigation scheduling, improved water-use efficiency, and sustainable crop production in arid and semi-arid regions. Although the FAO-56 Penman-Monteith equation (ETo(PM)) is widely accepted as the reference method, its reliance on complete meteorological data limits its applicability in data-scarce agricultural systems such as those in eastern Morocco. This study proposes a practical solution for daily ETo estimation under data-limited conditions by evaluating machine learning approaches against traditional empirical models. Daily climatic data (2001-2025) were collected from airport meteorological stations, NASA POWER reanalysis, and on-farm sensors at four representative locations in Eastern Morocco (Oujda, Berkane, Taourirt, and Figuig). ETo was computed using the ETo(PM) equation. Three ML algorithms-Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)-were developed under multiple input scenarios guided by feature-importance analysis, which identified solar radiation (Rsn), maximum temperature (Tmax), and relative humidity (RH) as dominant predictors. To ensure realistic evaluation and avoid information leakage, a strict chronological split was applied, using 2001-2018 for training and 2019-2025 for independent testing. The best ML configurations were then compared with widely used empirical ETo equations (Hargreaves-Samani, Jensen-Haise, Priestley-Taylor, Makkink, and Turc). Results showed that XGBoost consistently outperformed SVR and RF, achieving the best balance between accuracy and computational efficiency. The three-input configuration (Rsn + Tmax + RH) produced near-reference performance across all locations (R-2 = 0.976; RMSE < 0.55 mm day(-1); training time approximate to 4.48 s; RAM approximate to 0.13 MB), while the reduced two-input configuration (Rsn + Tmax) maintained reliable performance (R-2 = 0.936; RMSE approximate to 0.60 mm day(-1); training time approximate to 0.33 s; RAM approximate to 0.13 MB). In contrast, empirical approaches exhibited poor predictive capability and weak transferability, with negative R-2 values and substantially larger errors (RMSE generally > 4 mm day(-1), exceeding 7 mm day(-1) in the weakest formulations). Overall, this machine learning-based framework provides a robust and operational alternative for daily ETo estimation in eastern Morocco, supporting efficient irrigation scheduling, improved agricultural water management, enhanced crop productivity, and sustainable agriculture in arid and semi-arid regions.