<p>The global ocean carbon dioxide flux (air-sea) has shown a slow upward trend. Based on more than 160,000 quality-controlled measurements of surface ocean carbon dioxide fugacity from 2000 to 2020, a satellite-based ocean–atmosphere carbon dioxide fugacity (fCO₂) retrieval algorithm was developed using machine learning methods. A comparative analysis was conducted among various machine learning methods, including XGBoost, random forest, light gradient boosting machine, feedforward neural network, convolutional neural network, and backpropagation neural network. Based on the best performance, the random forest algorithm was selected for model construction. Independent in situ validation showed that the model achieved a low root mean square error (RMSE = 14.35 µatm), a low mean absolute percentage error (MAPE = 2.61%), and a high coefficient of determination (R² = 0.86). The distribution of global air-sea carbon dioxide fugacity from 2000 to 2020 was reconstructed at a resolution of0.25° × 0.25°, and the air–sea carbon dioxide flux (FCO₂) of the global ocean during the period of 2000–2020 was further estimated at a resolution of 0.25°×0.25°. During the period of 2000–2020, the global ocean CO₂ uptake increased from 1.443 PgC/year in 2000 to 1.894 PgC/year in 2020, and the air-sea carbon dioxide flux in the entire study area increased by 31.2% over the 20 years. These comprehensive oceanic carbon sink datasets and new insights will support future research on ocean carbon sequestration and its climate regulation potential.</p>