Abstract: Karst carbon sinks play a key role in narrowing the global carbon-balance gap. Reconstructing DIC (dissolved inorganic carbon)/DOC (dissolved organic carbon) concentrations with machine learning and couple them with the hydrochemistry-runoff method to more precisely quantify basin-scale karst carbon-sink flux. This study focuses on the Lijiang River Basin in Guangxi, China. Based on two years of field measurements (September 2015 to September 2017), we compared six machine-learning algorithms and selected random forest to build DIC and DOC concentration models (R² reached 0.93 and 0.68). We then predicted monthly DIC/DOC in 2017 and, with the hydrochemistry-runoff method, estimated a carbonate carbon sink flux of 2.42 × 10⁵ (t/a, CO₂)and an intensity of 43.41 (t/ (a·km²), CO₂). The concentration prediction results are consistent with previous studies, supporting the reliability of both the model and the accounting. This study provides methodological innovation and a scientific basis for karst carbon-sink estimation, and offers a reference for application in sparsely monitored karst basins.