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In this paper, the accuracy of a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined. For this aim, a novel method named as SVM–FFA is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration (n¯), maximum temperature (T max) and minimum temperature (T min) as inputs. The predictions accuracy of the proposed SVM–FFA model is validated compared to those of Artificial Neural Networks (ANN) and Genetic Programming (GP) models. The root mean square (RMSE), coefficient of determination (R 2), correlation coefficient (r) and mean absolute percentage error (MAPE) are used as reliable indicators to assess the models’ performance. The attained results show that the developed SVM–FFA …