<p>Steam methane reforming (SMR) is the most widely employed method for industrial hydrogen production owing to its cost-effectiveness. Existing studies have primarily focused on operational conditions, with relatively less attention given to the structural configuration of the reformer. In this study, a computational framework integrating computational fluid dynamics (CFD) modeling with Bayesian optimization (BO) is proposed to simultaneously optimize the design and operational variables of an SMR reactor. A CFD model was developed by coupling and iteratively solving the furnace and tube domains to accurately simulate the heat transfer characteristics. A sensitivity analysis was conducted to identify the key design variables, followed by BO, to efficiently investigate the design space. Consequently, methane (CH₄) conversion improved 3.0% based on the optimization scope. When design variables were optimized, CH₄ conversion increased by 2.3%, while operating variables resulted in an improvement of 0.2%; the simultaneous optimization of both resulted in a total enhancement of 3.0%. The optimal steam-to-carbon ratio increased from 3.4 to 4.75 when design parameters such as tube spacing and diameter were also optimized, thereby highlighting the interdependence between reactor geometry and operating conditions. This study demonstrates the effectiveness of BO in optimizing high-fidelity CFD reactor models and highlights its applicability to other thermally driven systems.</p>