Due to climatic change, a variation in meteorological aspects influences the water requirement for crops, evapotranspiration, and water allocation of agro-meteorological and agriculture. Accurate estimation of Evapotranspiration has great importance to improve the utilization of water efficiently and irrigation scheduling. The main overarching goal of this paper is to investigate the abilities and applicability of three supervised machine learning models: Extreme Machine Learning , Multi-layer Perceptrons-Neural Network, Support Vector Machine to modeling the daily . Further, a three-layer multi-model ensemble machine learning approach is presented to predict evapotranspiration . The first layer consists of different statistical models to produce individual forecasts. The blending approach is employed to create an ensemble of the forecasts generated by the initial layer to produce probabilistic forecasts. In the second layer, three ensemble models are trained for prediction of by using the previous layer predictions and training data. In the third-layer, accuracy is estimated by tuning the parameters of second layer ensemble model. It has been analyzed that all statistical models showed effectiveness in high performance for modeling everyday (e.g. Nash-Sutchliffe efficiency (NSE)= 0.93-0.99, coefficient of determination (r ) = 0.93-0.99, Accuracy (ACC) = 80-99, Mean Square error (MSE) = 0.0103-0.1516). Particularity, the ensemble method with SVM achieved good accuracy (99.46% to 99.72%) to predict the daily and correlation coefficient is closed to 1 on training, validation, and testing datasets. Its root means square error (RMSE) (0.0085 to 0.0935) and Mean Absolute Error (MAE) (0.0614 to 0.0639) are minimum as compared to other ensemble machine learning models.