This preprint presents a generative AI framework for inferring the interior state of the ocean from surface observations. The method leverages score-based diffusion models to probabilistically reconstruct subsurface temperature, salinity, and velocity fields from satellite data. By learning conditional probability distributions of interior states given surface inputs, the framework enables detailed and uncertainty-aware reconstructions of ocean dynamics from surface information alone. Applications span from scientific analysis to decision-making tools for autonomous underwater operations.