Abstract: Accurate prediction of crop actual evapotranspiration (ETa) and crop coefficient has great significance for designing irrigation plans and improving the water resources use efficiency. To improve the accuracy for predicting actual evapotranspiration and crop coefficient of maize under mulched drip irrigation, in this study, a Stacking Ensemble Learning Model (LSM) was developed to estimate evapotranspiration and crop coefficient of maize under drip irrigation with plastic film mulch. The LSM model included four classical machine learning methods including Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Adaboost (ADA). The maximal information coefficient (MIC) method was applied to calculate the MIC value between ten proposed features, including days after sowing, average temperature, plant height, leaf area index, solar radiation, extraterrestrial radiation, relative humidity, surface soil temperature, surface soil water content and wind speed at 2 m, and maize evapotranspiration. The MIC values were used to evaluate the importance of ten features. The results showed that in the test dataset the LSM model improved the coefficient of determination (R2) and decreased Normal Root Mean Square (NRMSE), Mean Absolute Error (MSE), and Mean Square Error (MSE), compared to SVM, RF, and ADA model. The BP model had the lowest R2 and the highest NRMSE. It revealed that the LSM model obtained the highest precision for modeling maize evapotranspiration, followed by SVM, ADA, and RF model, and BP model had the poorest performance for modeling maize evapotranspiration. Similarly, compared to four classical machine learning models, the LSM model increased R2 and decreased NRMSE, MSE, and MAE, indicating that LSM increased the precision for modelling maize crop coefficient under drip irrigation with film mulch. The MIC values of days after planting, average daily air temperature, leaf area index, plant height, and solar radiation were higher than those of the other features. It indicated that the five features above are important for maize evapotranspiration. Besides, compared to the LSM model with input of five top features, the LSM model with input of all the ten features didn’t show any obvious improvement in model simulation since the R2 was increased little and the NRMSE value was decreased by less than 0.05. The average crop coefficient values obtained by the LSM model with input of five top features were increased by 4%, 0, and −4.3% at developed stage, midseason stage, and late stage of maize, respectively, compared to the actual value. However, the crop coefficient values based on FAO-56 recommendation were 17.3%, 8.3%, and 13.8% lower or higher than actual crop coefficient in maize developed stage, mid stage, and late stage, respectively. This result indicated that the average crop coefficient values of LSM model with input of five top features were closer to actual crop coefficient value than that modified by FAO-56. Thus, the LSM model with input of days after planting, average daily air temperature, leaf area index, plant height, and solar radiation was recommended to estimate evapotranspiration and crop coefficient of maize under drip irrigation with plastic film mulch.