Computation of crop evapotranspiration (ETc) is a necessary for irrigation planning and agricultural water management. However, ETc is complex and dynamic depending on many meteorological variables, so it is challenging to estimate accurately. Therefore, artificial intelligence could be a method to overcome these complexities. Consequently, the main objective of this study was to evaluate the performance metric of four artificial intelligence algorithms, including k-nearest neighbor (kNN), support vector machine (SVM), random forest (RF) and adaptive boosting (AB) using eight different input combinations for daily sugar beet ETc estimation. The input combinations included daily data for crop coefficient, maximum and minimum air temperature, solar radiation, wind speed, and maximum and minimum relative humidity. The values were obtained from field experiments conducted at cumra, Konya in Central Anatolia, Turkey in 2012 and 2013 that focused on optimal water supply conditions for sugar beet production. The performance metric of four artificial intelligence methods were compared with a soil water balanced model, based on FAO methodology. All the artificial intelligence methods evaluated (kNN, SVM, RF and AB) provided satisfactory results with R2 in the range 0.799-0.992, mean squared error 0.048-1.273 mm d-1, root mean square error 0.219-1.128 mm d-1 and mean absolute error 0.156-0.832 mm d-1. The result show that the performance metrics of the artificial intelligence models get good performances even if the number of the input variables is applied less in the models. Among the input combinations, the SVM model with crop coefficient, maximum and minimum air temperature, solar radiation and wind speed (combination 5) shows better modelling accuracy than kNN, RF and AB models. Therefore, the SVM method is considered to be appropriate for estimation of daily sugar beet ETc in semiarid region.