aspg-18-2918-1792589500 20261021133140 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 13 2 Ahmed Saeed Alabed Assistant Professor, Information Systems and Computer Science Department, Ahmed Bin Mohammed Military College, Qatar Rajesh Kumar Samala Professor, Department of uGDX, ATLAS SkillTech University, Mumbai, Maharastra, India Asha KS Associate Professor, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India Sorabh Sharma Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Amit barve Associate Professor, Department of Computer Science and Engineering, Parul University, Vadodara, Gujarat, India Deepak Minhas Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India Key challenges in Wireless Sensor Networks (WSNs) include reduced dormancy, energy efficacy, reportage worries, and network lifetime. To solve the issues of energy efficiency and network longevity, more study of cluster-based WSNs is required. In order to address the challenges and constraints of WSNs, creative approaches are needed. WSNs use machine-learning techniques because of their unique characteristics. These characteristics include high communication costs, low energy reserves, high mobility, and frequent topological shifts. The current method picks cluster heads at random at the beginning of each cycle, not considering the remaining energy of these nodes. It is possible that the newly chosen CH nodes will have the lowest energy level in the network and will die off fast as a result. Energy is wasted while communicating over long distances between cluster heads and the BS, which occurs frequently in a big network due to Internet of things. This would mean that WSNs have a finite lifespan. Therefore, to increase the network's longevity and efficiency, we propose a machine-learning-based strategy called energy proficient crossbreed clustering methodology (ECCM). The experimental results reveal that the ECCM is superior to the LEACH approach, increasing residual energy by 35%, extending network lifetime by 37%, and increasing throughput by 15%. 2024 52 59 2918 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.130204 https://www.americaspg.com/journal/18/article/2918 https://www.americaspg.com/storage/01719357249.pdf