Edge–Cloud Intelligence Synergy: An AI-Driven Architecture for Scalable and Resilient Multi-Cloud Enterprise IT - Ravi Kumar Amaresam (International Journal of Computational and Experimental Science and Engineering, 2026)
The interplay of edge computing, artificial intelligence, and multi-cloud architecture fundamentally changes IT operating models that characterize the enterprise by removing constraints of the centralized cloud infrastructure. The conventional cloud-centric solutions have difficulty supporting real-time processing requirements, handling the deployment of latency-sensitive applications, and distributed data generation patterns. This article proposes a combined architectural model that brings AI inference and event processing to the edge locations and uses multi-cloud platforms to train models, orchestrate them, and perform massive analytics. The suggested layered architecture includes local intelligence in the form of edge nodes, centralized coordination in the form of cloud platforms, and orchestration mechanisms that allow the dynamic distribution of workloads within the heterogeneous environment. Issues of implementation include compression of models of resource-constrained edge devices, multi-cloud strategies to fall over, distributed domain security frameworks, and MLOps practices to support lifecycle management. The architectural patterns show how business firms can gain greater responsiveness, operational reliability, and cost efficiency due to intelligent coordination of edge and cloud resources. The framework gives practitioners practical advice on how to operationalize distributed AI systems and sustain governance, prevent vendor lock-in, and scale transformation of enterprises across various industry settings.