<p>UWOC systems use underwater optical communication to perform high-speed low-latency data transmission for underwater applications while suffering from absorption scattering and turbulent effects in aquatic environments. A traditional design of UWOC systems that uses separate optimization of transmitter and receiver components along with fixed models cannot effectively adjust to changing underwater conditions. The paper reviews end-to-end learning approaches that use autoencoder-based neural networks for improving UWOC system performance. Autoencoders optimize transmitter and channel modeling and receiver components as a joint system and enable real-time environmental adaptations that enhance bit error rate (BER) performance while extending distances and making systems more resilient. This document presents an overview of autoencoder constructs and integration frameworks alongside performance evaluation criteria along with applied cases which exhibit how machine learning UWOC platforms deliver superior outcomes than established standards. The advancement of future solutions recognizes the need for minimal design structures combined with automated learning approaches which use adaptive training approaches and collaborative research between different fields to solve issues related to limited data availability and fencing requirements and performance generalization problems. Underwater communication networks of the next generation can be built through the transformative capabilities of end-to-end learning based on autoencoders.</p>