(untitled) (Emerging Indexing of Global Multidisciplinary Journal, 2026)
The rapid proliferation of the Internet of Things (IoT) has necessitated a paradigm shift from centralized cloud computing to decentralized edge-fog-cloud architectures. This research article provides an extensive investigation into the integration of Edge Intelligence and Blockchain technology within the Industrial Internet of Things (IIoT) and next-generation communication systems. As the volume of data generated by industrial sensors, wearables, and autonomous systems grows exponentially, traditional architectures face severe bottlenecks in latency, bandwidth, and security. We explore the theoretical foundations of computation offloading, resource allocation, and continuous learning at the network edge. Special attention is given to the deployment of real-time Digital Twins and the role of Federated Learning in maintaining data privacy while ensuring high-fidelity predictive maintenance. The study further examines the security implications of edge intelligent systems, proposing blockchain-based reputation frameworks to mitigate trust issues in decentralized data ecosystems. By synthesizing current literature on mobile edge computing and industrial work safety, this article develops a holistic methodology for cross-domain standardization. The findings suggest that on-demand deep learning frameworks and collaborative cloud-edge pipelines are essential for achieving the low-latency requirements of Industry 4.0 and 6G networks. This research serves as a definitive guide for researchers and practitioners aiming to navigate the complexities of secure, intelligent, and scalable industrial networks.