Global cybersecurity infrastructure is seriously threatened by malware's quick spread and growing sophistication. Conventional manual reverse engineering techniques are time-consuming, labour-intensive, and have trouble scaling against fileless, automated, polymorphic, and metamorphic malware variants. This paper offers a sophisticated AI-driven malware reverse engineering framework that combines machine learning (ML), deep learning (DL), and behavioural analysis techniques to automate vulnerability identification, malware detection, code analysis, and function classification. The suggested system analyses malware binaries using both static and dynamic analysis techniques by utilizing deep neural networks and clever feature extraction techniques. Additionally, the system includes behavioural analytics Using automated pattern recognition to enhance the classification of malware families and identify dangers that haven't been seen before. Compared to conventional reverse engineering tools, experimental study shows that the suggested method greatly shortens malware analysis times while retaining high accuracy in detecting malicious intent and suspicious behaviours. The findings demonstrate how AI-powered automation may enhance real-time threat intelligence, scalability, and detection efficiency. In the end, this study highlights how artificial intelligence may revolutionize cybersecurity operations by replacing reactive protection mechanisms with proactive, intelligent, and automated threat detection systems.

Keywords: Threat Intelligence, Malware Classification, Reverse Engineering Systems, Vulnerability Detection, Automated Threat  Analysis, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Cybersecurity, Static Analysis, Dynamic Analysis, and Neural Networks.