Underwater optical wireless communication (UOWC) has difficultiesinfulfillingultra-reliablelow-latencycommunication(URLLC) standards owing to channel distortions, including absorption, scattering, and oceanic turbulence. This paper presents a deep reinforcement learning (DRL) approach utilizing proximal policy optimization (PPO) to concurrently adjust transmit power and coding rate in a point-to-point UOWC system employing hybrid automatic repeat request (HARQ) protocols chase combining (CC-HARQ) and incremental redundancy (IR-HARQ) capitalizing on statistical channel information and signal-to-noise ratio feedback, structured as a Markov decision process (MDP)with rewards that penalize power consumption and delay infractions. By reducing the long-term average power while adhering to stringent delay constraints (e.g., 99.9% dependability at 13 dBm in pristine marine conditions), the approach enables energy-efficient and dependable UOWC for Beyond 5G (B5G) and 6G applications, such as ocean monitoring.