This research aims to improve underwater acoustic communication using deep learning. Due to an increase in undersea operations, dependable communication systems have become more important. The undersea environments complexity reduces the efficacy of underwater audio communication, despite its widespread use. Using mathematical equations and approximations, the underwater sound pathway has been modeled. These projects aim to enhance underwater communication systems by better understanding the underwater audio channel. In this study, we investigate the abilities of device learning and deep studying to investigate and accurately replicate the underwater acoustic channel by making use of real-world underwater data. This is done by analyzing the results of the study. The information has been compiled with the aid of using a combination of strategies, which include machine learning and in-depth reading. In particular, the Deep Neural Community (DNN) and long quick term memory (LSTM) modeling strategies are used in order to achieve the goal of simulating the underwater audio channel. The results of the trials demonstrate that these models are capable of accurately modeling the underwater acoustic communication channel. Furthermore, the findings suggest that deep learning models, particularly LSTM, are better models in terms of mean absolute percentage error. The vast majority of the currently available UWSN routing protocols use a classical routing strategy.