Carbon cycling is one of the major biogeochemical processes driven by bacteria. Autotrophic bacteria convert carbon dioxide (CO2) into organic compounds that are used by heterotrophs. Mixotrophic bacteria can employ both autotrophy and heterotrophy for growth. The characterization of the lifestyle of individual cells is essential to understand the microbial activity and thus reveal the implication of bacteria in the carbon flux. In this study, we used groundwater bacteria to investigate the potential of Raman-D2O labeling in combination with chemometrics to identify the carbon assimilation strategies of bacteria. Classification models were built using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Autotrophs assimilated a significantly higher amount (mean C-D ratio between 16.63 and 21.69%) of deuterium than heterotrophs. The C-D signal only provides information about the activity since it appears in the Raman-silent region, where no interference with the taxonomic information is expected. The classification between autotrophs and heterotrophs achieved an overall accuracy of 96.3%. In the validation step with an independent dataset containing species not included in the model, the PCA-LDA model achieved 100% accuracy. This demonstrated that the C-D signal contributed to the identification of autotrophic and heterotrophic bacterial cells. This work reports a robust, rapid, and nondestructive approach for the identification of single cells based on their carbon acquisition strategies. The present study foresees the potential of Raman-D2O labeling as a promising method for automated discrimination of in situ functional activities of bacteria in environmental systems.