This paper looks at how artificial intelligence (AI) and machine learning (ML) can be used to improve the fusion of danger information. AI and ML are strong technologies that can automate and improve the study of huge amounts of different types of data. This makes it possible to find trends, outliers, and new threats that might not be picked up by standard methods. AI systems can take in, connect, and rank danger data from a variety of sources, including open-source intelligence, internal logs, and external feeds, by using supervised, uncontrolled, and reinforcement learning algorithms. AI-driven threat data fusion also makes it easier to evaluate and make decisions about threats in real time, which lets security teams act quickly to threats that are changing. AI systems can get useful information from uncontrolled data sources like danger reports, forums, and social media sites by using deep learning methods. Also, using AI and ML together in processes that combine threat intelligence lets them keep learning and adapting to new threat environments. Adaptive algorithms can change detecting limits, improve classification models, and add new danger signs based on how they see things happening and how they receive input. The coming together of AI and ML technologies with the fusion of threat intelligence has a huge amount of potential to make defense stronger. AI-driven fusion helps companies stay ahead of their enemies and stop new cyber threats by eliminating boring tasks, adding to the work of human researchers, and giving them fast, context-rich insights.