The continuous increase in energy consumption has brought worldwide attention to its significant environmental effect, which is triggered by the increase in greenhouse gas emissions, global warming, and rapid climate change. As such, more energy efficient buildings are required to minimize the energy consumption of heating and cooling. The present study introduces a set of machine learning-based models to predict the heating and cooling loads in buildings. This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines. The comparisons were conducted as per mean absolute percentage error (MAPE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE) and root-mean squared error (RMSE). The significances of the capacities of the machine learning models are evaluated using two-tailed student’s t-tests. Eventually, a holistic evaluation of the machine learning models is conducted using average ranking algorithm. Results demonstrate that the radial basis function network outperformed the afore-mentioned machine learning models significantly.