By 2030, internet-connected devices are projected to exceed 30 billion, increasing the demand for efficient and sustainable Wireless Sensor Networks (WSNs). Traditional algorithms often struggle with the NP-hard optimization problems introduced by complex systems, leading to interest in nature-inspired swarm algorithms. This study explores the use of the Honey Badger Algorithm (HBA), introduced in 2020, for optimizing multi-hop routing in WSNs using Matlab 2023b. HBA selects clusters, elects cluster-heads, and designates second-layer cluster-heads to enhance data transmission. Across 12 simulated scenarios in three different area sizes, WSNs with and without HBA were compared using metrics such as network lifespan, residual energy, and packet delivery ratio. Results show that while HBA underperforms in small areas, it significantly boosts performance in larger networks, extending coverage up to 24% compared to just 1% without it.