Due to the increasing importance of knowing the amount of global solar radiation (GSR) that is incident on solar panels; shortterm data, such as hourly global solar radiation (HGSR), is essentially required to obtain more accurate and reliable powergeneration prediction. Nowadays, Machine Learning (ML) methods are becoming a huge trend for data forecasting. Therefore,in this paper, a comparison between Collares-Pereira & Rabl empirical model modified by Gueymard (CPRG) and ML methodsfor HGSR estimation in Eskişehir city in Turkey is conducted. Artificial Neural Network (ANN), Regression Tree (RT), andSupport Vector Regression (SVR) are ML methods that are used to predict HGSR. Besides, hourly metrological andgeographical parameters for the year 2014 are taken as inputs in the training models. The inputs are solar time, solar hour angle,Julian day number, daily GSR, longitude, latitude, hourly average humidity, hourly temperature, and hourly pressure. Todemonstrate these techniques, a comparison is implemented using MATLAB software with the help of existing toolboxes.Finally, this study proves that ML methods outperform the CPRG model, not to mention they have far more accurate results.Although almost all ML models gave similar results, SVR was the best among them with a correlation coefficient of 0.979532for the training set and 0.978244 for the testing set. In a nutshell, ML are very efficient methods in that should be taken intoconsideration to perfectly estimate HGSR.