Evapotranspiration (ET) is a key ingredient of the water cycle, which makes it an indispensable part of water resources management and irrigation scheduling, especially in arid and semiarid environments. Countries, such as Egypt, grappling with limited water resources, impose constraints on water utilization, often discerned through diverse modelling methodologies. Ensuring the precision of model outputs is contingent upon accurate Evapotranspiration (ET) data, as numerous models leverage ET for input or validation, and the imperative alignment with field-scale applications necessitates obtaining ET data at a corresponding scale. Several methods have been developed for estimating ET and they vary widely in procedures, devices, complexity, and purpose. Compared to Egypt, this study will be set up to review the quantified ET under local environmental conditions, evaluate weather-based reference Evapotranspiration \(\left( {{\text{ET}}_{{\text{o}}} } \right)\) estimation methods, and investigate the developed novel methods for estimating \({\text{ET}}_{{\text{o}}}\) using a gene expression programming (GEP) model. Recently, Remote Sensing data have been used to estimate daily evapotranspiration values, overcoming the limitations of climate parameters and field data scarcity. The objective of this study is to evaluate the various dynamics employed to define the spatial and temporal variation in evapotranspiration in response to climate change through the application of Artificial Intelligence and Remote Sensing techniques. Considering the intricate water challenges in Egypt, the main objective of this review is to pinpoint lacunae in the comprehension of evaporation in Egypt and propose efficient recommendations. This review of the Precise estimation of \({\text{ET}}_{{\text{o}}}\) will be used as a vital tool for supporting the decision-making process in the future management of water resources, and on the other hand will have a positive effect on the rest of the limited water resources of Egypt.