The rapid evolution of cyber threats, coupled with the increasing complexity of distributed computing environments, has necessitated a paradigm shift in enterprise security strategies. Zero Trust Security Architecture (ZTSA), which operates on the principle of "never trust, always verify," has emerged as a robust framework to mitigate modern attack vectors. However, traditional Zero Trust implementations often struggle with scalability, dynamic policy enforcement, and real-time threat adaptation. The integration of Artificial Intelligence (AI) into Zero Trust frameworks introduces a transformative approach by enabling adaptive, context-aware, and predictive security mechanisms. AI-augmented Zero Trust architectures leverage machine learning, behavioral analytics, and automation to continuously evaluate trust levels, detect anomalies, and enforce granular access controls. This review explores the convergence of AI and Zero Trust, highlighting architectural components, implementation strategies, and challenges. It further examines how AI enhances identity verification, network segmentation, and threat intelligence, while addressing issues such as data privacy, model bias, and operational complexity. By synthesizing current research and industry practices, this article presents a comprehensive overview of AI-driven Zero Trust systems and their role in securing next-generation digital infrastructures.