,accurate estimation of plant water needs will have a great effect on reducing the problem of water crisis. Despite the importance of evapotranspiration in water resources planning and management, its dependence on climatic factors on the one hand and the influence of these factors on each other has made it difficult to estimate evapotranspiration. In this regard, based on the FAO Penman-Monteith relationship, the monthly potential evaporation-transpiration rate in the selected synoptic stations was calculated from the monthly meteorological data and as an input to the hybrid meta-exploratory models including the artificial neural network (ANN). , Evaluated Support Vector Machin (ESVM), Rain Forest (RF), Deep Learning (DL), Meta Combined Learning Model (Stacking) and Generalized Linear Model (GLM) are used. From the results, it was observed that the all reference evaporation and transpiration (ETo) prediction models (GLM, Stacking, DL, ESRV, RF, ANN-MLP) are highly efficient in all stations. However, the Stacking model has very high coefficient of determination (Gorgan: 0.996, Gonbad: 0.99, KordKoi: 0.99, Bandr-Torkman: 0.996, AliAbad: 0.991 and Minodasht: 0.992) and the lowest MAE (Gorgan: 0.25, Gonbad: 0.08, KordKoi: 0.25, Bandr-Torkman: 0.8, AliAbad: 0.33 and Minodasht: 0.97) and lowest RMSE(Gorgan: 6.849, Gonbad: 9.919, KordKoi: 9.671, Bandr-Torkman: 6.561, AliAbad: 9.123 and Minodasht: 8.73) in all the studied stations. It was also observed that after the Stacking model in all stations, the RF model and then the DL model have higher accuracy than the rest of the models.