The rapid convergence of Cyber-Physical Systems (CPS) with the Industrial Internet of Things (IIoT) has necessitated the evolution of sophisticated monitoring frameworks, prominently manifest in the form of Digital Twins (DT). While Digital Twins provide a transformative mechanism for real-time monitoring and predictive maintenance, their implementation in complex environments remains fraught with challenges regarding security, data integrity, and architectural scalability. This article explores the integration of blockchain-based access management, fog computing infrastructures, and generative artificial intelligence to address these critical deficiencies. By synthesizing existing research on multi-fidelity data fusion and secure provenance schemes, this study presents a comprehensive architectural framework designed to support the next generation of industrial applications. The proposed model utilizes fog computing to facilitate low-latency data processing while leveraging blockchain to ensure decentralized, immutable auditability of sensitive sensor data. Furthermore, the inclusion of generative intelligence for sensor fusion allows for the construction of high-fidelity models that are resilient to the noise and uncertainty inherent in real-world deployments. Through a rigorous examination of the literature, including systematic mapping studies and formal testing protocols, this research identifies the essential requirements for standardization-aligned DT ecosystems. The analysis concludes that the unification of these distributed technologies is imperative for achieving fault-tolerant, scalable, and trustworthy CPS environments, providing a roadmap for practitioners and researchers to navigate the complexities of Industry 4.0 and beyond.