Underwater sensor networks have received extensive attention owing to their promise for application to marine exploration, submarine navigation, and pollution monitoring. In an open ocean underwater environment, the targets to be monitored are undefined. Therefore, it is necessary to investigate how sensor nodes adjust their positions autonomously in accordance with the changes in the environment and targets to achieve optimal monitoring quality. We propose a fish-swarm-inspired underwater sensor network deployment algorithm using density-based spatial clustering of applications with noise (DBSCAN). Firstly, inspired by the operation mode of an artificial fish-swarm system, sensor nodes autonomously cover all the events by simulating physiological behaviors such as swarm, follow, and prey. Secondly, considering the complexity of the underwater environment and also to reduce the number of nodes participating in the movement and to avoid the blind movement of nodes, the DBSCAN model is introduced to achieve the sharing of sensed information among the nodes that communicate by the single-hop or multihop mode, thereby enhancing the global search ability of nodes. Finally, a large number of experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm can effectively solve the problem of underwater sensor deployment, and has the advantages of fast convergence and strong scalability.