Cyber threat hunting, or actively looking for bad people inside an organization’s network, is very important in today’s digital world. Using machine learning (ML) in decision systems makes the process of looking for online threats more efficient and effective. ML is used by decision systems to automatically find strange behavior. This makes the job of human researchers easier and allows for real-time danger reaction. Machine learning-driven decision systems help an organization stay ahead of cyberattackers by constantly learning from past data and reacting to new threats. ML also makes it easier to prioritize alerts by giving threats risk scores based on how likely they are to happen and how bad they could be if they do. This lets security teams focus on the most important problems. Machine learning techniques also help improve hunting methods by finding new attack trends and offering possible hunting theories. With this preventative method, security teams can see threats coming and stop them before they become full-scale attacks. There are still problems with putting machine learning (ML) into cyber threat hunting decision systems, like not having enough data, finding ways to trick adversaries, and being able to understand the models. To solve these problems, cybersecurity experts, data scientists, and subject experts need to work together across disciplines to create strong machine learning-based decision systems that can successfully fight cyber dangers. ML-driven decision systems are becoming more and more important for companies to protect their digital assets and keep their organizational stability as cyber risks change.