<p>The increasing density and heterogeneity of Internet of Things (IoT) deployments impose stringent requirements on latency, energy efficiency, and reliability within edge computing environments. These systems typically operate under highly dynamic, bursty workloads, where rapid fluctuations in queue occupancy and resource utilization can lead to congestion, increased latency, and deadline violations. Conventional task offloading strategies, which rely on static heuristics or data-intensive machine learning models, often fail to achieve real-time adaptability, low computational overhead, and decentralized decision-making simultaneously. This study introduces an event-driven neuromorphic task offloading framework for edge-IoT systems utilizing Spiking Neural Networks (SNNs). The offloading problem is formulated as a multi-objective optimization process that jointly addresses latency, energy consumption, and deadline compliance. An SNN-based controller is integrated into edge nodes to approximate optimal decisions via spike-driven temporal dynamics, enabling adaptive, energy-aware orchestration without centralized optimization or continuous model inference. The proposed framework is evaluated using a hybrid simulation environment that integrates YAFS and Brian2. Theoretical analysis demonstrates bounded decision dynamics and sparse computational complexity. Experimental results indicate that the proposed approach reduces latency by up to 26%, decreases energy consumption by 32%, and improves task success rate by 25% under high-load conditions compared to heuristic and machine learning-based baselines. These findings suggest that neuromorphic, event-driven decision mechanisms offer an effective and scalable alternative for real-time orchestration in dynamic and resource-constrained edge-IoT environments, particularly under congestion-prone conditions anticipated in next-generation large-scale deployments.</p>