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Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mo>(</mo><mi>E</mi><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula> is an important factor in determining DI. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>E</mi><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> forecasting predicts field water consumption and enables proactive irrigation decisions, offering guidance for water resource management. However, implementation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>E</mi><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> forecasting faces challenges due to complex calculations and extensive meteorological data requirements. This project aims to develop a machine learning system for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>E</mi><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> forecasting. The project involves studying <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>E</mi><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> methods and identifying required meteorological parameters. Historical meteorological data and weather forecasts were obtained from meteorological websites and analyzed for accuracy after preprocessing. A machine learning-based model was created to forecast reference crop evapotranspiration. The model’s input parameters were selected through path analysis before it was optimized using Bayesian optimization to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather forecasts, and one that corrects the weather forecasts. All three models achieved good accuracy, with root mean square errors ranging from 0.52 to 0.81 mm/day. Among them, the model based on weather forecast had the highest accuracy; the RMSE six days before the forecast period was between 0.52 and 0.75 mm/day, and the RMSE on the seventh day of the forecast period was 1.12 mm/day. In summary, this project has established a mathematical model of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>E</mi><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></semantics></math></inline-formula> prediction based on machine learning, which can achieve more accurate predictions for within a few days.