Metalloporphyrins and Porphyrins (MpPs) have received increasing attention as potential photocatalysts for hydrogen and oxygen evolution and the reduction of carbon dioxide (CO2). Recent studies have found that both HOMO/LUMO energy levels and energy gaps are important factors controlling the performance of MpPs photocatalysts. Since pure quantum chemistry methods consume a large amount of computational resources and cannot learn directly from previously accumulated data, in this study, we proposed a deep transfer learning approach to rapidly predict the HOMO/LUMO energy levels and energy gaps based on two quantum chemistry databases. To complement the open-source Porphyrin-based Dyes Database (PBDD), which contains porphyrin structures designed as photosensitizers, we curated a new database, the Metalloporphyrins and Porphyrins Database (MpPD), in which MpPs were specifically designed as potential photocatalysts. We also adapted two state-of-the-art chemical deep learning models to accommodate more complex systems of MpPs. The PorphyBERT model and PorphyDMPNN model, both pretrained with PBDD and fine-tuned with MpPD, performed satisfactorily in predicting HOMO/LUMO level and energy gap. We concluded the paper by recommending 14 MpPs as potential photocatalysts for CO2 reduction, whose structures have similar value in out-of-sample model prediction and DFT calculations.