The use of AI in countering increasingly heightened threat of cyber- terrorism is investigated throughout the research study. How cyber-terrorism’s nature is constantly evolved through intelligence vulnerabilities to disrupt service and cause a security breach is also highlighted through several experiments in this study. Finally, current legal frameworks and locatesgaps and challenges are addressed here regarding the regulation of AI-driven cyber-security measures. AI-driven strategies were also articulated in this research to enhance the capabilities to defend against cyberterrorism through examining its role in threat detection, automated response, and enhancement of threat intelligence. The need for adaptive cyber-security frameworks is depicted in experimental result underlining continuous improvement and international collaboration for mitigating emerging cyber threats and finally developing a Hybrid AI-driven Model Integrating Deep Learning and Reinforcement Learning (DRL) to prevent cyber-terrorisms. Federated learning has been utilized to securely share threat intelligence, while explainable AI (XAI) has been incorporated to ensure transparency, interpretability, and compliance with regulations. The model is designed to be robust and adaptive to counteract emerging cyberterrorism threats effectively.