Abstract Coupled ocean‐biogeochemical models are essential for understanding marine ecosystems, yet they often suffer from persistent biases due to poorly constrained empirical parameters. To address this limitation, we introduce Neural‐BGC, an observation‐driven neural network emulator for hybrid physical‐biogeochemical modeling. Neural‐BGC is trained on quality‐controlled ocean profiles from the World Ocean Database. The model employs a cascaded architecture to predict dissolved oxygen and nitrate concentrations from physical state variables and spatiotemporal coordinates. We couple this emulator to the Regional Ocean Modeling System and evaluate the hybrid framework in two contrasting regions: the Arabian Sea and the Canary Current Upwelling. The hybrid model reproduces observed spatial patterns and seasonal variability, while capturing features such as oxygen minimum zones and coastal nutrient fronts. Notably, Neural‐BGC often outperforms a tuned NPZD model in simulating the mean biogeochemical state. This framework offers a computationally efficient, data‐driven alternative for high‐fidelity regional and global ocean BGC modeling.