The Food and Agriculture Organization of the United Nations (FAO) estimates that population growth will reach 11.2 billion by the year 2100, which will contribute to food and agricultural product demand, making irrigation optimization essential. In this context is highlighted the evapotranspiration parameter determined by the FAOPM method. However, a precise measurement of this parameter requires several climatic parameters that can not be available in rural areas, in which, a promise solution belongs in approaches that use just a few climatic parameters which can be obtained by satellites and weather stations combined with machine learning models. In this research, the MLP (Multilayer perceptron) e SVM (Support Vector Machines) models were used to model the reference evapotranspiration with satellite and weather stations data under two approaches: the local approach where the models are trained and tested on the training location, and the regional approach where the models trained on the training location were applied on a test location. These approaches were applied in two experiments: the first on a temperate climate zone, and the second on a tropical climate zone. The results indicate that the MLP model stood out when compared with the SVM model in all tests realized, in which, the models trained with the climatic parameters of temperature and radiation obtained the metrics of R2 of 0.6568, RMSE of 0.1103, and MAE de 0.0882 for the temperate climatic zone experiment and metrics R2 of 0.7391, RMSE 0.1266, and MAE of 0.1063 for the tropical climate zone experiment on the first approach which demonstrate the potential of using only these parameters to model de evapotranspiration. For the second approach the MLP model could be applied on the tropical climate zone in which the metrics R2 of 0.7158, RMSE of 0.1592, and MAE of 0.1428 were obtained. Yet the result obtained by the models applied on the temperate climate zone was inconclusive which indicates that for the conditions of this location the models can’t be applied with the second approach