The Internet of Things (IoT) has simplified communication across diverse settings. However, the challenging nature of acoustic communication in underwater environments makes device interaction difficult. As a result, many researchers focus on enhancing the lifespan of IoT networks in these aquatic settings. Packet loss in acoustic networks leads to retransmissions, which reduces the lifespan of all types of network environments. This paper introduces the Acoustic Network Optimization by BAT Data Prediction Model ANOBDPM designed to address the challenges of underwater communication by generating missing data at the node of the cluster center. This is achieved by learning and analyzing sensor data ANOBDPM operates in two main phases learning and clustering The BAT algorithm is employed to group IoT nodes effectively underwater network optimization thrives in dynamic conditions as the BAT algorithm utilizes ultrasonic waves for daily life work and movements. The back propagation neural network predicts lost packets based on previous incoming packets. Experiments were conducted in different environments with varying numbers of nodes, areas, etc. Results show that the use of learning increased packet delivery by 29.41% and the number of rounds by 22.4%.