Proton-electron transfer (PET) reactions are rather common in chemistry and crucial in energy storage applications. How electrons and protons are involved or which mechanism dominates is strongly molecule and pH dependent. Quantum chemical methods can be used to assess redox potential (Ered.) and acidity constant (pKa) values but the computations are rather time consuming. In this work, supervised machine learning (ML) models are used to predict PET reactions and analyze molecular space. The data for ML have been created by density functional theory (DFT) calculations. Random forest regression models are trained and tested on a dataset that we created. The dataset contains more than 8200 quinone-type organic molecules that each underwent two proton and two electron transfer reactions. Both structural and chemical descriptors are used. The HOMO of the reactant and LUMO of the product participating in the oxidation reaction appeared to be strongly associated with Ered.. Trained models using a SMILES-based structural descriptor can efficiently predict the pKa and Ered. with a mean absolute error of less than 1 and 66 mV, respectively. Good prediction accuracy of R2 > 0.76 and >0.90 was also obtained on the external test set for Ered. and pKa, respectively. This hybrid DFT-ML study can be applied to speed up the screening of quinone-type molecules for energy storage and other applications.