To predict ET and PT processes a combined DFT-ML technique was used. We looked at a wide range of quinone type compounds at different charge and protonation states. For all these systems the redox potential and acidity constant were computed. We presented a dataset consisting of about 8200 compounds made up of 15 backbones decorated with 1-2 functional groups taken from a list of 9 groups. The data were extensively examined from a chemical and statistical perspective. As a result, we were able to train Random Forest models according to the structures and attributes. The molecular space can be described by chemical properties and/or structural characteristics. The most crucial features for the predictions of the acidity constant and redox potential, respectively, are the HOMO and LUMO energy levels. Strong predictability is demonstrated on the external test sets by models created using SMILES strings. While on the internal test sets, great accuracy was reached by all trained models. Although we tested the method on quinone derivatives, it applies to other types of redox compounds as well.