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In today’s developing IoT world, wireless sensor interactions are critical. To ensure full system IP compatibility, industry efforts are focused on standardizing the WSN protocol suite and enabling the second generation of IoT devices to be produced. As a result of their robust communications and conformance to less verbose WSN features such as sleep cycles required to keep end nodes’ total energy consumption low, the WSN protocols are strong contenders for use as a highly useable implementation layer for the IoT. Existing smart systems make use of system assets for critical IoT functions. Compared to a wired configuration, wireless sensors are easier to install and provide more device flexibility. As sensor technology advances, WSNs will become a critical component of the IoT and an essential tool for achieving the IoT paradigm’s goal. It’s also crucial to think about whether or not a WSN’s sensors should be fully linked into the IoT’s. For WSNs, a multi-objective optimization technique for selecting the most energy-efficient CH is described. The CHs for each lattice were first selected using a candidate CH selection method. Energy waste and data redundancy were also decreased by using multi-hop networking. An inventive new method has also been devised to ensure perfect implementation. The Evolutionary Algorithm (EA) model and optimization techniques used in this work provided an energy-efficient Machine Learning (ML) strategy for selecting WSN CH. First, the CH of each lattice was calculated using a candidate CH selection method. According to the results of the simulation, the suggested model’s performance is superior to traditional approaches in terms of network lifespan, energy efficiency, and reduced consumption. Key Word: IoT, smart applications, wireless sensor network, clustering, routing protocol and optimizations.