The Internet of Things (IoT) is a collection of various sensors connected to the internet that share information. In a large-scale IoT network, data is collected through the wireless sensor network (WSN), and the aggregated data is sent from the sink to the next level of IoT for processing. Clustering is utilized to cut down on energy use, network redundancy, interference, and collision in WSN and improve network lifetime, scalability, and data aggregation. In addition, multi-hop communication is more effective for networks with sensors that cover a broad region. The Multi-Hop Low Energy Adaptive Clustering Hierarchy brings about a reduction to the transmission distance and prolongs the network lifetime. This particle swarm optimization (PSO) technique is effective for determining the most effective solutions for a particular problem. The particles in the PSO embody the candidate solutions tend to move through their solutions space (in several directions) in different velocities. A distributed multi-hop cluster-based routing algorithm that takes advantage of the PSO and the Lightening Search Algorithm is developed in this work. The proposed method optimizes the clustering process and achieves energy efficiency, as demonstrated by the experimental results. Reduced end-to-end delay and lower packet loss rate whereas the lifespan network and cluster count are improved.