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The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO₂ emission-reduction goals. This study built a data-driven stacked random forest regression model for estimating gridded global fossil fuel CO₂ emissions. The driving variables include the annual features of column-averaged CO₂ dry-air mole fraction (XCO₂) anomalies based on their ecofloristic zone, night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS), terrestrial carbon fluxes, and vegetation parameters. A two-layer stacked random forest regression model was built to fit 1° gridded inventory of open-source data inventory for anthropogenic CO₂ (ODIAC). Then, the model was trained using the 2014–2018 dataset to estimate emissions in 2019, which provided a higher accuracy compared with a single-layer model with an R² of 0.766 and an RMSE of 0.359. The predicted gridded emissions are consistent with Global Carbon Grid at 1° scale with an R² of 0.665, and the national total emissions provided a higher R² at 0.977 with the Global Carbon Project (GCP) data, as compared to the ODIAC (R² = 0.956) data, in European countries. This study demonstrates that data-driven random forest regression models are capable of estimating anthropogenic CO₂ emissions at a grid scale.