Neuromorphic computing and edge intellige- -nce have emerged as transformative technologies for developing energy-efficient intelligent systems capable of supporting nextgeneration digital infrastructures. Traditional cloud-centric architectures often experience high latency, excessive energy consumption, and limited real-time processing capability in large-scale intelligent environments. This research proposes a Neuromorphic Edge Com- -puting Architecture designed to optimize computational efficiency, adaptive intelligence and energy-aware processing for modern distributed systems. The proposed framework integrates neuromorphic processors, edge computing mechanisms, and intelligent resource management techniques to achieve low-power real-time computation and adaptive decision-making. Unlike conventionalcompu computing architectures, the proposed model mimics biological neural systems to improve processing efficiency and reduce computational overhead. The architecture supports intelligent applications including smart healthcare, autonomous systems, industrial automation, and Internet of Things environments. Experimental evaluation demonstrates improved energy efficiency, reduced latency, scalable edge intelligence, and enhanced computational performance compared to traditional cloud-based systems. The proposed research contributes to the advancement of sustainable intelligent computing frameworks capable of supporting future digital ecosystems, adaptive automation, and high-performance distributed computational environments.