<p>Wireless Sensor Microsystem Networks represent the infrastructure of next generation Internet of Things, where communication, energy module and computation are incorporated into compact microsystem based sensor nodes. The microsystem nodes enable miniaturized hardware integration, scalable deployment and low power consumption in large scale environments. Meanwhile, due to limited computational resources, efficient clustering and routing mechanism is essential to ensure reliable communication with enhanced network lifetime. This paper proposes a unified hybrid framework to achieve energy aware clustering and intelligent routing in Internet of Things based Wireless Sensor Microsystem Networks. In order to enhance network lifetime and estimate better intra and inter-cluster communication, the Cluster Head selection and clustering strategy are optimized. Initially, the sensor network is clustered using a quantum-inspired fuzzy C-means model that introduces probabilistic perturbation to enhance exploration during cluster formation. Moreover, the crossover-boosted revolutionary optimization algorithm selects the optimal cluster head that integrates exploration–exploitation trade-offs for detecting the energy-efficient cluster nodes. The routing path of the cluster is analyzed by a Dual-Encoder Transformer with an Attention-Guided Energy Awareness framework to predict optimal multi-hop paths thereby minimizing delay and energy consumption during network transmission. The effectiveness of the proposed method is computed using various measures. Experimental results demonstrate that the proposed model achieves a network lifetime of 32,300 rounds, and energy consumption of 35 Joules. Furthermore, the proposed framework outperforms existing methods and other baseline approaches, showing significant improvements in energy efficiency, network lifetime, and data transmission performance.</p>