Abstract <p>Photocatalytic reduction of carbon dioxide (CO₂) presents a sustainable strategy for mitigating atmospheric CO₂ while producing value-added fuels. However, the process involves tightly coupled radiative, kinetic, and hydrodynamic phenomena, making it a demanding target for simulation and reactor optimization. This review critically analyzes recent numerical approaches to CO₂ photoreactor modeling with a focus on how specific simulation techniques—spanning CFD platforms (e.g., COMSOL, Fluent, OpenFOAM), radiation models (DOM, Monte Carlo, RTE), and kinetic frameworks—have been applied to different reactor geometries. We highlight key correlations between modeling choices and reactor performance metrics, such as 15 times increase in methanol yield via bubble diameter tuning in twin reactors, and a 60% enhancement in light utilization efficiency through fiber layout optimization in monolith designs. A structured comparison is presented across photoreactor types, modeling strategies, and parameter sensitivities, supported by summarizing tables. Limitations in current simulation practices—including numerical resolution, mesh sensitivity, and inadequate coupling of light–reaction–flow—are critically discussed. The review identifies gaps in modeling convergence analysis, radiation transport fidelity, and experimental validation fidelity, and proposes best practices for integrating simulations with experimental feedback. By linking modeling methodologies directly to performance outcomes, this work serves as a roadmap for the simulation-driven design of scalable, efficient CO₂ photoreactors. It also outlines how future research can benefit from digital twins, AI-assisted simulations, and real-time feedback control systems.</p> Graphical Abstract