Today, with the ever increasing?frequency, scale, and sophistication of these cyber-threats, traditional perimeter-based security models are inadequate in preventing enterprise systems and sensitive content from the rising threats. The?growth in hybrid cloud environments, remote workers, and edge devices has increased the attack surface, requiring real-time, adaptive cybersecurity to be a mission critical priority. In this context, Zero Trust Architecture (ZTA) has been fast-gaining momentum as a fundamental change in approach, with?a philosophy that focuses on never trust, always verify to ensure least privileged access and continuous authentication of users, devices, and workloads. This work investigates the design of self-adaptive cybersecurity architectures where Zero Trust models are combined with threat detection algorithms?based on AI, enabling a proactive and intelligent automation of defense mechanisms. We discuss how to bake machine learning into the detectives to offer context-aware, real-time enforcement and dynamic policy adaptation — anomaly detection, behavior analytics, and natural language processing are?some of the examples of machine learning techniques to embed within Zero Trust frameworks. Security Information and Event Management (SIEM), User and Entity Behavior Analytics (UEBA), as well as automated incident response systems, are reviewed for increasing resilience?in complex IT environments. The rest of the paper is organized as follows: in section 2, we illustrate case studies as well as the experimental results to show that AI integrated ZTA can decrease detection-to-response time, restrict false positives, and capable of scaling?protection for the insider threat, lateral movement, and zero-day exploitation. This research is a part of a broader body of knowledge combining digital transformation?needs with cybersecurity strategy alignment and proposed best practices for public and private sectors.