Underwater Wireless Sensor Networks (UWSNs) play a significant role in applications such as ocean monitoring, environmental assessment, offshore infrastructure inspection, disaster management, and maritime surveillance. Despite their wide range of applications, the underwater environment presents several communication challenges, including limited bandwidth, long propagation delays, dynamic network topology, high transmission errors, and restricted energy resources. These constraints reduce the effectiveness of conventional networking approaches designed for static environments. Recent developments in intelligent data-driven techniques have introduced new opportunities to improve the adaptability and efficiency of underwater communication systems. This review presents a comprehensive analysis of intelligent computational approaches applied to UWSNs, with emphasis on machine learning, deep learning, and reinforcement learning methods. The study examines their contributions to routing optimization, energy management, communication reliability, localization, and network security. A comparative review of existing research is provided to highlight performance trends, advantages, and existing limitations of different techniques. Furthermore, the paper discusses current research challenges and identifies promising future directions for developing scalable, reliable, and energy-efficient underwater sensing systems that support next-generation Internet of Underwater Things (IoUT) applications.

Keywords: Underwater Wireless Sensor Networks, Machine Learning, Deep Learning, Reinforcement Learning, Intelligent Routing, Energy Optimization, Internet of Underwater Things.