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Machine learning methods are applied to obtain adsorption energies of different chemical species on (100), (111), and (211) FCC surfaces of several transition metals and Pb. Based on information available in databases containing adsorption energies obtained via first-principles calculations, we implemented MLPRegressor, XGBRegressor, Support Vector Regressor, and Stacking machine learning models. The fourth model is created from the combination of the previous three through a Stacking technique. In a broader context, our results showed the robustness of machine learning models and the ability of these methods to speed up the screening materials to specific goals, at a low computational cost. We emphasize the ability of our models to predict the adsorption energy for different systems. Due to their generality of them, we were able to make ion predictions on metallic surfaces, taking into account the influence of different functionals. This capability is of special significance due to the difficulty of calculating the correct energy for charged systems by traditional atomistic simulations. From then on, we made predictions for important chemical species in the CO2 electroreduction process, such as the radical anion CO2 −•, an important intermediary for obtaining new products in view of a negative carbon footprint.