Accurate estimation of potential evaporation and transpiration (ETp) in an efficient way is quite important in water balance, crop water requirement and agricultural management studies. Therefore, the aim of this study was to improve the accuracy of several ETp estimation equations namely Hargreaves-Samani, Belaney-Criddle, Ivanov and Thornthwaite using spatial and meteorological data in Ramsar, Kermanshah, Hamedan, Semnan, Mashhad, Shiraz, Ardabil, Urmia, Zanjan, Yazd and Rasht stations, across the Iran, in seasonal and scales. The FAO-Penman-Monteith method was selected as the evaluation measure. The correction approaches were based on regression and support vector regression, relative and parallel hybrid framework. The type of spatial and meteorological variables had a significant impact on increasing the accuracy of correction equations, as an example, using a corrected regression equation eliminating of the wind component decreased the variance of errors for 38.18% comparing to original Ivanov's method with its all included variables. The regression correction method had better performance compared to the ratio method, such that the average increase of the Willmott index for all input states from the ratio method to the regression for the Thornthwaite, Blaney-Criddle, Ivanov and Hargreaves-Samani methods was equal to 42.67, 5.45, 25 and 3%, respectively. The structure of parallel hybrid framework in all methods reduced the error in determining potential evapotranspiration, and the final approach including the parallel framework of optimal modes of support vector regression and simple regression had significant accuracy. The final approach showed that Torrent White, Blaney-Criddle and Ivanov methods have underestimation and Hargreaves-Samani overestimates ET_P. The structure of the correction method has a significant importance in increasing the precision of potential evapotranspiration and water demand estimation for improved planning.