The recent advancements in underwater and maritime research are significantly enhancing the capabilities of autonomous systems, particularly in challenging environments such as the Arctic and underwater settings. There is a notable shift towards leveraging machine learning and advanced sensor technologies to improve navigation, obstacle avoidance, and communication in these domains. For instance, the integration of 2.5D sonar with Control Barrier Functions is proving to be a game-changer for Autonomous Underwater Vehicles (AUVs), enabling efficient real-time navigation in complex underwater environments. Additionally, the development of low-cost AUVs like Lo-MARVE is democratizing access to marine exploration technologies, making them more accessible for environmental monitoring and research. Furthermore, the use of Gaussian Process Classification for predicting acoustic communication performance in AUVs is advancing the reliability of underwater communication, which is crucial for coordinated missions. These innovations collectively push the boundaries of what autonomous systems can achieve in underwater and maritime contexts, paving the way for more robust and efficient operations in these challenging environments.