Abstract: This paper presents a systematic review of the fundamental technological paradigms and developmental trajectories for target recognition in underwater optical images. Firstly, the theoretical underpinnings of the field is elucidated, addressing the fine-grained features and long-tailed distribution of underwater targets. Subsequently, three core technological pathways are summarized: sample enhancement, model architecture optimization, and transfer learning. Despite notable advancements in data enhancement, architecture optimization, and knowledge transfer, challenges persist, including inadequate optimization of fine-grained and long-tailed category identification and limited adaptability to complex underwater environments. To substantially enhance the precision and robustness of underwater target recognition, future efforts must focus on large-scale dataset construction, few-shot learning, and open-set incremental recognition. These efforts will provide robust technical support for the establishment of smart oceans and the monitoring of marine ecology.