The yields of chemical reactions are highly dependent on the mixing pattern between reactants. Herein, we report the modification of a meso-micromixing interaction reaction model which is applied in batch reactors by leveraging the flow characteristics in the continuous reactors. Both experimental and model-predicted yields were compared using the classical Villermaux−Dushman method in a self-designed split and recombination reactor. This modified model significantly reduced the error in predicted product yields from approximately 15% to within 3%, compared to a model containing the micromixing term only. The effects of flow rates and reactor structure parameters on mixing performance were analyzed. We found that increasing flow rates and the degree of twist in the mixing element’s grooves, as well as decreasing the cross-sectional area of grooves, improved mixing performance. The optimization of reactor flow rates and structural parameters was achieved by combining Gaussian process regression and Bayesian optimization with the modified model. This approach provided higher target product yields for consecutive reactions, while simultaneously achieving a lower pressure drop in the reactor. Corresponding combinations of reactor parameters were also identified during this process. Our modified model-based optimization methodology can be applied to a diversity of reactors, serving as a reference for the selection of their structure and operational parameters.