Effective ballast water management is crucial for maintaining marine ecosystem balance and complying with international regulations, yet optimizing Ballast Water Management Systems (BWMSs) performance remains a significant challenge in maritime operations. This paper presents an adaptive online machine learning approach for optimizing BWMSs performance in maritime applications. Our framework relies on real-time sensor data from ships and ports to keep its forecasting models accurate. We use different training strategies to balance precision and efficiency. These include continuous updates, scheduled updates, and updates triggered by certain thresholds. The system creates probabilistic forecasts, which give us a clearer view of prediction uncertainty. This helps us make better, more informed decisions. In extensive tests with real-world data from 473 ports in 65 countries and 23 ships, our approach proved to be highly effective. Among the models we used, the Temporal Fusion Transformer performed best, achieving the lowest Root Mean Squared Error, Mean Absolute Percentage Error, and Continuous Ranked Probability Score. We also created visualizations to show how ship and port performance changes over time and across different locations. These visualizations highlight the system’s adaptability to different conditions and provide actionable insights. Overall, this work marks a major step forward in efficient and environmentally friendly ballast water management, supporting sustainable practices in global shipping.