Reference evapotranspiration (ET0) is a critical parameter for water resource management and irrigation scheduling. Accurate estimation of ET0 has challenged scientists over the years due to its high sensitivity to climatic variations. Classical methods for estimating ET0 mainly rely on empirical models with a significant number of parameters, which has hampered their use in many cases. Regarding its importance and strong relationship with global food security, this topic has attracted the attention of many researchers. The development of simple models with a low number of parameters or taking advantage of artificial intelligence algorithms has been the aim of different researchers, as it is in this paper, where two approaches are implemented to estimate reference evapotranspiration. The first one is based on the use of classical system identification models, namely linear and nonlinear AutoRegressive models with eXogenous variables (ARX and nonlinear ARX). For the second approach, AI-supported system identification models are used, in which neural networks' performances are used to develop multilayer and deep neural network models for nonlinear system identification. The four models show a high accuracy, with a system fitting exceeded 98%.