We aim to achieve resource recycling by capturing and using CO2 generated in a chemical production and disposal process. We focused on CO2 conversion to CO by the reverse water gas shift-chemical looping (RWGS-CL) reaction. This reaction proceeds in two steps (H2 + MOx ⇆ H2O + MOx-1; CO2 + MOx-1 CO + MOx) via a metal oxide that acts as an oxygen carrier. High CO2 conversion can be achieved owing to a low H2O concentration in the second step, which causes an unwanted back reaction (H2 + CO2 CO + H2O). However, the RWGS-CL process is difficult to control because of repeated thermochemical redox cycling, and the CO2 and H2 conversion extents vary depending on the metal oxide composition and experimental conditions. In this study, we developed metal oxides and simultaneously optimized experimental conditions to satisfy target CO2 and H2 conversion extents by using machine learning and Bayesian optimization. We used transfer learning to improve the prediction accuracy of the mathematical models by incorporating a data set and knowledge of oxygen vacancy formation energy. Furthermore, we analyzed the RWGS-CL reaction based on the prediction accuracy of each variable and the feature importance of the random forest regression model.