The Earth’s oceans present formidable challenges to exploration and understanding. Their vastness, depth, and hostile conditions, such as under thick ice layers in polar regions or extreme depths under high pressure, make them difficult to navigate. It is essential to monitor the environment and collect data to assess marine biodiversity, ecosystem health, and the global ecosystem’s interdependencies. These are all affected by climate change, pollution, and human activities. Autonomous underwater vehicles (AUVs) have transformed ocean exploration. They navigate and collect data autonomously in challenging environments. Robust decision-making algorithms are essential for cooperative mission planning among AUVs. It is equally critical to select and implement algorithms that govern collaborative behaviors such as target acquisition and formation maintenance. This paper investigates algorithms for cooperative target search and capture missions, ensuring mission integrity between AUVs. The study employs simulation using the network simulator OMNeT++ to analyze and address these challenges, facilitating enhanced collaboration and efficiency in AUV operations.