Abstract: The electrocatalytic CO₂ reduction reaction （eCO₂RR） represents a pivotal technology for closing the anthropogenic carbon cycle and enabling renewable energy storage. Thus, the development of cost-effective, high-performance carbon-based electrocatalysts has emerged as a critical research frontier. Fossil resource-derived polycyclic aromatic hydrocarbons （PAHs） sourced from coal, petroleum, and their industrial byproducts have emerged as ideal precursors for constructing high-performance electrocatalysts for the CO₂ reduction reaction （eCO₂RR）, owing to their structural tunability, low cost, abundant availability, and intrinsic richness in polycyclic aromatic frameworks. This review provides a comprehensive overview of recent advances in the conversion of fossil resource-derived PAHs into advanced carbon-based electrocatalysts for eCO₂RR. It first outlines the physicochemical characteristics of diverse fossil resource-derived PAHs feedstocks and the associated pretreatment protocols, elucidating how processes-including acid-base deashing, pre-oxidative cross-linking, and selective solvent extraction-enhance carbon purity and thermal stability. Subsequently, key synthetic strategies are summarized, including the construction of hierarchical pores via chemical or physical activation, morphological regulation via templating, and the modulation of electronic structures and active sites via defect engineering. Furthermore, the intrinsic relationships between physicochemical properties （e.g., pore architecture, active site coordination） and catalytic performance （e.g., mass transfer, intermediate adsorption, selectivity） are analyzed. The indispensable role of advanced characterization and theoretical calculations in unraveling reaction mechanisms is also highlighted. Finally, the challenges associated with fossil resource-derived PAHs-based carbon materials are discussed, including the high complexity of precursors, insufficient understanding of reaction mechanisms, low selectivity toward multi-carbon products, and limited scalability. Future directions focusing on molecular design, integrated in-situ characterization and computation, machine learning, and scalable applications are also outlined.