With the faster growth of fifth-generation (5G) and sixth-generation (6G) wireless communication systems, there are unprecedented requirements for ultra-low energy consumption wireless and microwave networks facing an increasing demand for enabling extreme-bandwidth data rates, massive connectivity, and low-latency communications. For optimization problems such as network routing, microwave resource allocation and antenna parameters adaptation, even though the general optimality of the solutions can be proven or demonstrated while optimizing directly with traditional algorithms, they often converge slowly and get trapped in local optima. In response to these issues, this paper presents an Energy Efficient Wireless and Microwave Network Framework using Advanced Hybrid Salp Swarm–Whale Optimization (HSSWO) algorithm. It provides a hybrid method that uses SSA with its good exploration ability and WOA for adaptively exploiting the routing paths, transmission power, microwave antenna parameters, and spectrum assignment. It further adds an AI-assisted network evaluation module for intelligent decision-making. The experimental results show that the proposed HSSWO framework attains 95.82% energy efficiency, a 41.6% improvement in network lifetime, throughput of 12.47 Gbps, packet delivery ratio of 99.21%, end–to–end latency of only 0.58 ms and a packet loss of only 0.69%. Moreover, the utilization of the proposed method leads to a reduction of optimization time for an average of 31.8% as well as improving convergence speed by an average of 36.4% over state-of-the-art optimization methods. The proposed HSSWO framework is an efficient energy-aware architecture for next-generation wireless and microwave communication systems as these results confirm.

The rapid evolution of next-generation wireless communication systems has increased the demand for intelligent microwave components and energy-efficient wireless architectures capable of supporting ultra-high data rates, low latency, massive device connectivity, and adaptive resource management. Conventional optimization approaches often experience slow convergence, high computational complexity, and limited adaptability when optimizing multiple communication parameters simultaneously. To address these challenges, this paper proposes a Machine Learning-Assisted Adaptive Pelican Optimization (ML-APO) framework for the joint optimization of microwave components and wireless communication architectures. The proposed framework integrates an Adaptive Pelican Optimization Algorithm with a machine learning-assisted surrogate prediction model that rapidly estimates communication performance metrics, thereby significantly reducing computational overhead during the optimization process. The optimization simultaneously considers transmission power, antenna parameters, microwave component characteristics, spectrum allocation, routing efficiency, and communication reliability through a multi-objective fitness function. The adaptive search strategy of the Pelican Optimization Algorithm effectively balances global exploration and local exploitation, enabling faster convergence toward optimal network configurations while avoiding premature convergence. Extensive simulation results demonstrate that the proposed ML-APO framework achieves 96.41% energy efficiency, 43.8% improvement in network lifetime, 13.56 Gbps throughput, 99.43% packet delivery ratio, 0.49 ms end-to-end latency, and 0.54% packet loss, while reducing optimization time by 35.8% and improving convergence speed by 39.6% compared with existing optimization methods. These results demonstrate that the proposed framework provides an efficient, scalable, and intelligent solution for designing high-performance microwave components and next-generation wireless communication architectures suitable for future 6G and beyond wireless networks.