Biofuels are promising alternatives to fossil fuels due to diminishing reserves and increasing environmental concerns. This review focuses on recent progress in understanding the combustion kinetics of oxygenated biofuels derived from biomass. The review begins with fundamental concepts and research methodologies in reaction kinetics, intended as a primer for engineering researchers. Subsequently, kinetic studies from the past decade on typical oxygenated biofuels are summarized, including alcohols, fatty acid methyl esters (FAMEs), ketones, ethers, and carbonates. Emphasis is placed on the influence of different oxygenated functionalities and their positions within the molecule on combustion characteristics and reaction pathways. Distinct reaction patterns for each class are highlighted. Alcohols exhibit a characteristic unimolecular dehydration reaction. FAME kinetics are similar to long-chain hydrocarbons, with unsaturation significantly impacting low-temperature oxidation. Ketone oxidation is influenced by the formation of resonance-stabilized radicals, while straight-chain ethers demonstrate a unique double negative temperature coefficient (NTC) behavior. Carbonates, relevant to lithium-ion battery safety, have gained research attention and can undergo a distinctive reaction pathway identified as CO₂ elimination reaction. To advance predictive kinetic models for biomass-derived oxygenated fuels, several targeted research directions are essential. First, there is a critical need to expand experimental datasets that capture the combustion behavior of diverse oxygenated compounds, particularly under low-temperature conditions. This must be coupled with enhanced combustion diagnostics capable of resolving key reaction intermediates characteristic of oxygenated fuel oxidation. Second, detailed quantum chemical calculations and theoretical explorations of potential energy surfaces are required to accurately determine reaction rate parameters for oxygen-involved pathways, which are often determinant in fuel decomposition and pollutant formation. Finally, progress in model predictability will depend on the adoption of advanced computational methods, including automated mechanism generation for complex oxygenated structures, systematic optimization frameworks leveraging experimental data, and the incorporation of physics-informed artificial intelligence approaches tailored to oxygenated fuel chemistries.