The increasing integration of digital technologies in finance, accounting, and healthcare systems has transformed operational efficiencies while simultaneously introducing unprecedented cybersecurity, privacy, and regulatory challenges. Zero-Trust Architecture (ZTA) emerges as a fundamental framework for securing microservices, cloud deployments, and critical infrastructure, emphasizing strict verification protocols and continuous monitoring (Kesarpu, 2025; Al-Shaer & Bou-Harb, 2021). Concurrently, Artificial Intelligence (AI) adoption in auditing, fraud detection, financial reporting, and healthcare data management offers the potential to enhance operational accuracy and decision-making but raises ethical, legal, and privacy concerns (Adelakun et al., 2024a; Akinsola & Ejiofor, 2024). This study systematically examines the convergence of ZTA and AI applications in financial and healthcare ecosystems, exploring the theoretical underpinnings, practical implementations, and observed outcomes. Methodologically, the research synthesizes findings from contemporary literature, integrating case studies and empirical evidence to construct a comprehensive conceptual framework. Results highlight the dual role of ZTA and AI: while ZTA strengthens system-level resilience and mitigates unauthorized access, AI enables predictive insights, anomaly detection, and regulatory compliance. Challenges including ethical considerations, integration complexity, legal frameworks, and operational scalability are critically analyzed. The discussion emphasizes the necessity of harmonizing technical security architectures with ethical AI governance, highlighting gaps in current regulatory practices and proposing pathways for future research. The study contributes a nuanced understanding of how ZTA and AI collectively enhance data integrity, fraud mitigation, and privacy protection, thereby informing policy, technical design, and operational strategy in the digital economy.