Artificial intelligence (AI) is providing unprecedented insights into previously hard-to-study ocean currents, marking a significant advancement in oceanography. A recent article from Eos.org highlights research demonstrating how AI modeling techniques, combined with observing system simulation experiment design concepts, can predict the behavior of influential currents like the Indonesian Throughflow. This current, vital for global ocean circulation and sea surface temperature as it connects the Pacific and Indian Oceans, is notoriously complex and expensive to measure using traditional methods. Researchers developed a deep learning model incorporating convolutional neural networks (CNNs) for data trend extraction and recurrent neural networks (RNNs) for analyzing changes over time. This approach proved substantially less computationally costly than conventional methods, while accurately recapitulating observed water transport trends. The study identified sea surface height as a key predictor for conditions in certain Indonesian straits, with the Maluku Strait emerging as a crucial passage influencing the entire system. This application of AI is corroborated by numerous other scientific reports. Institutions like Caltech, MIT, and Mercator Ocean are also leveraging AI and machine learning to improve ocean circulation understanding, navigate autonomous underwater vehicles, and create more accurate and faster ocean forecasts. The ability of AI to process vast, real-time datasets from diverse sources, including satellites and in-situ sensors, is revolutionizing marine research, offering benefits for climate studies, environmental monitoring, and marine resource management globally.