The accurate prediction of excited state properties is a key element of rational photocatalyst design. This involves the prediction of ground and excited state redox potentials, for which an accurate description of electronic structures is needed. Even with highly sophisticated computational approaches, however, a number of difficulties arise from the complexity of excited state redox potentials, as they require the calculation of the corresponding ground state redox potentials and the estimation of the 0-0 transition energies (E0,0). In this study, we have systematically evaluated the performance of DFT methods for these quantities on a set of 37 organic photocatalysts representing 9 different chromophore scaffolds. We have found that the ground state redox potentials can be predicted with reasonable accuracy that can be further improved by rationally minimizing the systematic underestimations. The challenging part is to obtain E0,0, as calculating it directly is highly demanding and its accuracy depends strongly on the DFT functional employed. We have found that approximating E0,0 with appropriately scaled vertical absorption energies offers the best compromise between accuracy and computational effort. An even more accurate and cost-effective approach, however, is to predict E0,0 with machine learning and avoid the use of DFT for excited state calculations. Indeed, the best excited state redox potential predictions are achieved with the combination of M062X for ground state redox potentials and machine learning (ML) for E0,0. With this protocol, the excited state redox potential windows of the photocatalyst frameworks could be adequately predicted. This shows the potential of combining DFT with ML in the computational design of photocatalysts with preferred photochemical properties.