As the sophistication of cyber threats has grown, security models have been pushed to their limits and organizations are embracing Zero Trust Network Architectures (ZTNA) that continually check users, devices and applications before providing access to critical resources. AI has done a great job in identifying and reacting to new threats but many AI security products are "black box" systems that do not allow security analysts to trust the system and hold it accountable for making decisions. Explainable Artificial Intelligence (XAI) mitigates this limitation by offering clear explanations to the results of threat detection systems that are interpretable and usable, fostering trust, compliance with regulations, and operational efficiency. This paper explores how XAI can be applied to enhance the continuous authentication, anomaly detection, threat intelligence and automated response capabilities that strengthen the Zero Trust philosophy in adaptive cyber threat detection. The research also covers important XAI techniques, adaptive machine learning methods, and new reinforcement learning and federated learning methods for ensuring robust and large scale cybersecurity systems. Furthermore, it analyses the advantages, obstacles and prospects of using XAI in Zero Trust environments. The results show how integrating explainability with adaptive threat detection improves decision transparency, decreases false positive rates, facilitates a more efficient response process, and contributes to more resilient, trusted, and intelligent cybersecurity frameworks that can handle today's constantly evolving and complex cyber threats.