The partial pressure of carbon dioxide (pCO₂) on the surface of the ocean is crucial for quantifying and evaluating the ocean carbon budget. Insufficient consideration of the effects at the sea area scale makes it difficult to comprehensively evaluate the spatiotemporal distribution characteristics and variation patterns of pCO₂. This study constructed a pCO₂ evaluation dataset based on LDEO measurement data and multi-source data. After conducting correlation testing on a global, far sea, and near sea scale, a ocean surface pCO₂ evaluation model was constructed using multiple linear regression, convolutional neural network, gated recurrent unit, long short-term memory network, generalized additive model, extreme gradient boosting, least squares boosting, and random forest. Performance evaluation indicates that the random-forest model consistently achieves the best accuracy across all spatial scales, yielding a global RMSE of 6.123 μatm and an R² of 0.986. In the open ocean, RMSE decreases to 4.699 μatm and R² rises to 0.988, whereas in coastal waters RMSE increases to 8.044 μatm and R² declines to 0.972. Based on this, the annual sea surface pCO₂ distribution of 0.25° × 0.25° from 2000 to 2019 was reconstructed. The reconstructed field shows a typical equatorial high/polar low pattern, as well as an overall upward trend consistent with independent observations, with acceleration particularly evident in specific regions of subtropical coastal oceans.