This paper presents an innovative AI-enabled edge computing framework that integrates healthcare monitoring and energy management in smart cities through adaptive binning techniques. The proposed system combines distributed IoT sensors, blockchain-based secure data transmission, and neuromorphic computing to create a scalable infrastructure for urban health monitoring and energy optimization. Our framework addresses critical challenges in existing systems, including data privacy, energy efficiency, and real-time processing capabilities. The SMILE (Secure Middleware for Intelligent Life Enhancement) middleware serves as the core orchestration layer, managing distributed sensor networks while maintaining data security through federated Byzantine fault tolerance mechanisms. Compared to baseline cloud-centric and edge-only architectures, the implementation shows significant improvements in processing efficiency (47% faster than traditional cloud systems), reduction in energy consumption (38% compared to standard edge deployments) and diagnostic accuracy (93.5% versus 85% baseline accuracy). Experimental validation in 14 international deployment sites shows the system’s adaptability to diverse urban environments with statistical significance \( p < 0.001 \) . The framework’s integration of adaptive histogram-based stream processing with custom neural networks enables effective management of distributed sensor networks while maintaining data security and system reliability.