Evapotranspiration is a critical natural process in agriculture. Every farmer has to deal with this in every stage of crop development. Proper estimation of soil and leaf’s-based Evapotranspiration (ETo) is required to be done. Traditionally it is being done based on climate forecasting only, but nowadays, many new techniques have been shown up by which this can be estimated more accurately. In this study, machine learning and deep learning models are being used to determine the ETo. Regression techniques are preferred over classification so that every possible value can be taken as an input for accuracy. Nowadays, many algorithms have proved their efficiency with the specific dataset. There is no unique algorithm that has established itself as suitable for every type of dataset. This study will help to find the best algorithm for getting accurate predictions on ETo. A few famous regression algorithms are being opted for this study, and the best resultant algorithm is determined for a given dataset. The highlighted algorithm based on the experiment should only be followed if similar climate attributes are encountered for any research and study.