CLIMATE-INDUCED LIMATE-INDUCED stress on irrigation water usage is escalating rapidly across the globe, especially in arid and semi-arid regions like Egypt, where climate change and water scarcity create unsustainable conditions for agriculture. Notably, the Development of innovative and practical methods for estimating reference, evapotranspiration (ETo) is important for efficient irrigation scheduling. This study evaluated the efficacy of six machine learning (ML) algorithms, Linear Regression (LR), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and XGBoost, in estimating ETo, utilizing long-term meteorological variables from publicly available datasets. Three established empirical models served as baseline comparisons: FAO Penman-Monteith (PM), Hargreaves (HA), and Blaney-Criddle (BC). Each model was assessed using historical daily meteorological data retrieved from the NASA POWER database, which provides reliable long-term climate records relevant to agricultural applications. Model performance was evaluated based on test using three statistical methics, coefficient of determimtion-(R), root mean square error (RMSE), and mean absolute error (MAE). When comparing the ETo estimation methods, the Blaney-Criddle (BC) equation used in combination with ML models displayed the most accurate predictions. In terms of ML algorithms, Random Forest (RF) consistently outperformed other algorithms with R * C <^> 2 = 0.98 and RMSE -0.12 mm/day when using the BC equation during testing. Support Vector Regression (SVR) performed well for all models as well. RF appeared to be the best ML algorithm, and the BC equation was the best ETo model for the study area and conditions studied. It supports the use of ML models to enhance ETo estimation with limited meteorological data, particularly evident in water-scarce surroundings such as Egypt. The current study aims to fill the gap of localized ETo estimation models in Egypt by comparing ML predictions to traditional empirical models using long-term climatic data, thus providing a valuable contribution to the body of research on precision irrigation through the use of imperfect use of data. A specific focus on the Behera Governorate of Egypt determines a relationship between prioritized adaptive irrigation models where temperature rises and variable rainfall, complexifying irrigation demands, and increasing evaporation. The main aim of this study is to evaluate and compare traditional ETo estimation equations and machine learning algorithms to determine the most accurate and robust method for ETo prediction in arid climates with limited data availability.