Accurate monitoring of fossil fuel CO₂ (ffCO₂) emissions is essential for tracking climate mitigation, yet natural carbon-cycle fluxes often obscure human-induced signals in atmospheric observations. This study presents a satellite-driven data assimilation framework that uses nitrogen oxides (NO_x = NO + NO₂) &minus; short-lived trace gases co-emitted with CO₂ &minus; to estimate ffCO₂ emissions. We estimate European NO_x emissions for 2021 by assimilating TROPOMI NO₂ observations into an Ensemble Kalman Filter (EnKF) framework, optimised within the GEOS-Chem atmospheric transport model. We use a computationally efficient offline treatment of NO_x chemistry, enabling large-ensemble inversions while retaining sensitivity to changes in photochemistry. Assimilating these data leads to a systematic reduction in the state vector uncertainty, with a mean ensemble-based error reduction of 4.2&ndash;5.7 %, and an overall improvement in model agreement with observations that corresponds to an annual correlation increase, ∆<em>r </em>=0.12. By leveraging sector-specific NO_x:CO₂ emission ratios, we translate our posterior NO_x flux estimates into improved ffCO₂ estimates that capture enhanced seasonal variability. Our inferred ffCO₂ emissions exhibit elevated values in autumn and winter, spatially concentrated over major source regions and consistent with surface temperature variability. Independent evaluation against <em>in situ</em> measurements confirms significant improvements in mean error statistics. While OCO-2 CO₂ column data remain dominated by biogenic signals, our NO₂-driven approach successfully isolates the fossil fuel component. This study demonstrates the potential of ensemble data assimilation and reduced-complexity chemistry to provide physically consistent constraints on European ffCO₂ estimates, establishing a vital foundation for future joint NO₂&ndash;CO₂ inversion systems.