The process employed to discover new materials for specific applications typically utilizes screening of large compound libraries. In this approach, the performance of a compound is correlated to the properties of elements referred to as descriptors. In the effort described below, we developed a simple and efficient machine learning (ML) model for predicting adsorption energies of CH4 related species, namely, CH3, CH2, CH, C, and H on the Cu-based alloys. The developed ML model predicted the DFT-calculated adsorption energies with 12 descriptors, which are readily available values for the selected elements. The predictive accuracy of four regression methods (ordinary linear regression by least-squares (OLR), random forest regression (RFR), gradient boosting regression (GBR), and extra tree regression (ETR)) with different numbers of descriptors and different test-set/training-set ratios was quantitatively evaluated using statistical cross validations. Among four types of regression methods, we have found that ETR gave the best performance in predicting the adsorption energies with the average root mean squared errors (RMSEs) below 0.3 eV. Strikingly, despite its simplicity and low computational cost, this model can predict the adsorption energies on a range of Cu-based alloy models (46 in total number) as calculated by using DFT. In addition, we show the ML prediction for the differences in the adsorption energies of CH3 and CH2 on the same surface. This would be of great importance especially when designing the selective catalytic reaction processes to suppress the undesired over-reactions. The accuracy and simplicity of the developed system suggest that adsorption energies can be readily predicted without time-consuming DFT calculations, and eventually, this would allow us to predict the catalytic performances of the solid catalysts.