Models are widely used to simulate ocean conditions, such as oxygen levels, but these models often contain biases due to limited observations and simplified assumptions. In this study, we applied a type of artificial intelligence called neural network to correct these biases in simulated ocean oxygen levels. By using long-term observations from the Northern California Current System, our approach significantly improved the accuracy of the model, especially in identifying low-oxygen (hypoxic) events along the Pacific Northwest coast (Washington and Oregon)—an area that frequently experiences this problem. Our findings show that machine learning can be a powerful tool for improving ocean model predictions, especially for tracking changes in important environmental indicators like oxygen.