In recent years, AUV-assisted underwater sensor networks (AUV-UWSNs) have gained significant attention due to their ability to monitor and collect crucial data from the oceanic environment. However, efficiency of these networks has hindered certain issues which include energy consumption problems, low communication range and rational routing/ clustering problems. Two of the major challenges affecting other aspects of AUV-UWSNs are the lack of a proper way to choose the right cluster heads and guarantee sound data transmission. This paper proposes a high-efficiency clustered routing technique that tackles these problems by utilizing the Spider Wasp Optimizer (SWO) for optimal cluster head selection and the Deep Fuzzy Gorilla Troops Self-Guided Generative Adversarial Clustering Network (DFGTS-2GACN) for efficient data transmission. The SWO enhances the choice of cluster heads with respect to energy consumption and the DFGTS-2GACN applies deep learning and fuzzy logic control to facilitate data forwarding in the network with enhanced performance. Experimental performance shows 99% improvement compared with the existing methods. In this paper, an effective method to consider for increasing the data transfer rate while decreasing the energy consumption plus increasing the life cycle of the underwater network facilitated by the AUV in the underwater sensor networks is discussed, thus providing a viable solution to the problems of underwater communication.