Background: Rapid convergence of artificial intelligence (AI), cyber-physical systems (CPS), and distributed ledger technologies has created both unprecedented capabilities and complex vulnerabilities across domains that include clinical research, Internet of Medical Things (IoMT), agri-food supply chains, and financial services. The disparate literatures on AI-driven protection mechanisms, secure blockchain topologies, and CPS design patterns demand integrated theorization to inform both academic inquiry and practical governance (Syed & Faiza, 2024; Voulgaris et al., 2020).

 Objective: This research article synthesizes theoretical and applied knowledge from the provided references to present a unifying conceptual and methodological framework for AI-enabled resilience in cyber-physical and financial systems. The framework explicates how AI models, secure architectures, and CPS design patterns can act synergistically to mitigate cyber threats, preserve data integrity, and enable trustworthy automation.

 Methods: Using a rigorous integrative review approach grounded in the supplied references, the methodology reconstructs causal pathways between system components (AI agents, sensors, ledgers, network topologies) and adversarial threats. The paper uses systematic cross-domain mapping of threat vectors, defenses, and design principles, supported by close textual analysis of theory and applied studies in the corpus (Lee & Seshia, 2006; Khaitan & Mohan, 2021).

 Results: The synthesis yields a layered resilience architecture: (1) sensing and verification at the edge (IoMT & RFID-enabled traceability), (2) secure transaction and provenance layers (hierarchical blockchain topologies), (3) AI-driven anomaly detection and adaptive response, and (4) governance and auditability mechanisms for human oversight. This architecture addresses confidentiality, integrity, availability, and non-repudiation across domains, and demonstrates how finance-specific requirements for transactional integrity align with CPS safety constraints (Singh, 2025; Rajkumar & Lee, 2010).

 Conclusions: The integrated framework demonstrates that AI is not merely a detection tool but a coordinating agent that—if designed with CPS principles, embedded assurance, and secure ledger topologies—can materially enhance system resilience. Implementation requires domain-specific model assurance, explainability, and regulatory alignment. Future research should empirically validate cross-domain transferability, quantify trade-offs between model complexity and interpretability, and operationalize governance mechanisms for adaptive, AI-mediated security.