The machine learning (ML) can help find, prioritize, and fix security holes in digital settings that are complicated and always changing. The danger landscape is always changing, so traditional methods to risk management often can’t keep up. This calls for a more aggressive and flexible answer. With the help of very large datasets and advanced pattern recognition, machine learning algorithms can find small patterns that point to possible security holes, even when there aren’t any known signs. The suggested approach uses both controlled and untrained machine learning models to classify and rate risks using past data, system behavior, and information about the situation. As long as the system keeps learning, it gets a better sense of what is normal and what isn’t, which helps it adapt to online threats that are always changing. Furthermore, methods for anomaly spotting help find new and unpatched security holes, which is a very important part of protecting against new threats. Using natural language processing makes it easier for security warnings to be automatically analyzed. This helps security teams stay up to date on the newest security holes and fixes. This ML-driven vulnerability management system not only speeds up the process of finding possible risks, but it also makes the best use of resources by putting fixing flaws in order of how bad they are and how much damage they could cause. Organizations can improve their cybersecurity by using machine learning to find and fix weaknesses and stay strong against cyber threats that are always changing.