<p>The rapid digital transformation of modern society has significantly increased the complexity and frequency of cyber threats targeting individuals, organisations, financial institutions, healthcare systems, and government infrastructures. Traditional cybersecurity mechanisms are no longer sufficient to detect sophisticated and evolving attacks in real time due to their dependence on signature-based detection and manual analysis. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of strengthening modern cybersecurity frameworks through intelligent threat prediction, anomaly detection, and automated prevention strategies. This research article explores advanced AI-driven cybersecurity models designed for predictive threat detection and autonomous cyber defense mechanisms. The study examines the application of supervised learning, unsupervised learning, deep learning, and neural network architectures in intrusion detection, malware analysis, phishing prevention, behavioral analytics, and network traffic monitoring. Furthermore, the paper highlights the advantages, challenges, ethical concerns, and future scope of integrating AI with next-generation cybersecurity systems. The findings indicate that AI-powered cybersecurity infrastructures provide adaptive, scalable, intelligent, and real-time protection against emerging cyber threats while significantly improving detection accuracy and response efficiency.</p>