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The proliferation of heterogeneous edge equipment, ultra-dense IoT nodes and multi-vendors distributed computing infrastructures have introduced novel complexity to deploying scalable, reliable and persistently changing AI loads. Conventional centralized training approaches do not satisfy the needs of low-latency inference, privacy compliance constraints, and cross-domain interoperability at the lower extreme. This article proposes a Federated AI Orchestration model that will help to smartly coordinate the training, adaptation, and deployment of models in heterogeneous edge settings without the need to centralize data. The proposed structure combines real-time distributed ecosystems of continuous learning by integrating dynamic resource awareness, gradient exchange preserving privacy, adaptive resource partitioning, and federated optimization. Multi-layer orchestration engine aligns device heterogeneity, link uncertainty, and workload agility as well as provides a secure convergence of models. Experimental analysis shows that the training efficiency is better, communication overhead decreases, and resistance to data drift and adversarial contamination is higher than in traditional federated methods. The work adds a coherent, lightweight, and autonomous federated orchestration model that will allow future AI systems to work well, securely, and sustainably at the network edge supporting 6G next-generation, smart industries, critical infrastructure, and digital healthcare deployments.