In this study, a new soft computing model Gaussian process regression (GPR) was evaluated for modeling the total solar radiation (TSR) and exergy (CYRILLIC CAPITAL LETTER EF) in Hakkari province (the region with the highest sunshine duration), Turkey. For this purpose, meteorological data include average, maximum and minimum temperature (T-ave, T-max, T-min), relative humidity (H), sea level pressure (P), wind speed (W), and total sunbathing time (TST), wihch were used, and sensitivity analysis was applied for evaluating the results of TSR and CYRILLIC CAPITAL LETTER EF modeling. The results showed that all the input variables have significant impact on TSR and CYRILLIC CAPITAL LETTER EF modeling. Mean absolute percentage error and coefficient of determination (R-2) for TSR and CYRILLIC CAPITAL LETTER EF predicted by GPR were 1.51-7.02% and 0.97-0.95, respectively. Application of five-fold cross validation method showed that GPR model is able to predict the TSR and CYRILLIC CAPITAL LETTER EF with a small size of data, but for more accuracy, it is suggested to use more than 70% of total data set for training the models. This research showed that GPR has a good ability for modeling the TSR and CYRILLIC CAPITAL LETTER EF with high accuracy, and so the engineers can use this method for the TSR and CYRILLIC CAPITAL LETTER EF prediction without using the solar radiation or exergy-to-energy ratio.