Task allocation in smart manufacturing systems must operate under decentralized decision-making, dynamic workloads, and shared-resource constraints. In circular manufacturing settings, these challenges are further intensified because tasks compete for reusable, capacity-constrained assets, and machine selection also might affect processing energy. Although learning-based approaches have been explored for task allocation, improvements in predictive modeling do not necessarily translate into better allocation outcomes under decentralized negotiation. This work proposes an Edge-AI-driven decentralized task-allocation framework. We develop lightweight decision intelligence deployed at the machine level. It is developed progressively: first, a resource-aware heuristic establishes the decentralized bidding structure; a regression-based Edge-AI formulation then examines learned local bid approximation, and a compact autoencoder-regularized pairwise ranking model finally provides a learned correction to the analytical bid ordering. Each machine evaluates incoming tasks by using its processing capability, queue state, energy characteristics, and a compact signal representing contention over the reusable shared production asset. The framework is assessed using discrete-event simulation in scenarios characterized by high load and dependence on shared resources. Compared to the heuristic, the proposed ranking method increases completed tasks, reduces average tardiness, and lowers the deadline-miss rate, with statistically significant paired differences. Mean energy per completed task is also reduced. The results indicate that effective learning-assisted allocation depends not only on approximating local decision quantities, but also on shaping the relative preferences that determine negotiation outcomes.

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