Although CO₂ contributes significantly to global warming, it also offers potential as a raw material for the production of hydrocarbons such as CH₄, C₂H₄ and CH₃OH. Electrochemical CO₂ reduction reaction (eCO₂RR) is an emerging technology that utilizes renewable energy to convert CO₂ into valuable fuels, solving environmental and energy problems simultaneously. Insights gained at any individual scale can only provide a limited view of that specific scale. Multiscale modeling, which involves coupling atomistic‐level insights (density functional theory, DFT) and (Molecular Dynamics, MD), with mesoscale (kinetic Monte Carlo, KMC, and microkinetics, MK) and macroscale (computational fluid dynamics, CFD) simulations, has received significant attention recently. While multiscale modeling of eCO₂RR on electrocatalysts across all scales is limited due to its complexity, this review offers an overview of recent works on single scales and the coupling of two and three scales, such as “DFT+MD”, “DFT+KMC”, “DFT+MK”, “KMC/MK+CFD” and “DFT+MK/KMC+CFD”, focusing particularly on Cu‐based electrocatalysts as copper is known to be an excellent electrocatalyst for eCO₂RR. This sets it apart from other reviews that solely focus exclusively on a single scale or only on a combination of DFT and MK/KMC scales. Furthermore, this review offers a concise overview of machine learning (ML) applications for eCO₂RR, an emerging approach that has not yet been reviewed. Finally, this review highlights the key challenges, research gaps and perspectives of multiscale modeling for eCO₂RR.