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In this study, different Artificial Intelligence (AI) techniques including Feed Forward Neural Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), empirical models including Hargreaves and Samani (HS), Modified Hargreaves and Samani (MHS), Makkink (MK), Ritchie (RT) and conventional Multilinear Regression (MLR), were employed to model Reference Evapotranspiration (ET₀) in fourteen stations from several climatic regions in Turkey, Cyprus, Iraq, Iran and Libya. For this purpose, 12 parameters of monthly climate data were collected and used as input parameters to the models. The parameters were subjected to quality assurance tests to ensure their validity and acceptability. The study was conducted in three sections: (i) Sensitivity analysis was conducted to determine the dominant inputs. (ii) Single models were trained and their performances were accessed …