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In this paper, a new adaptive extreme learning machine (ELM) neural network-Fuzzy system framework is developed for effective and efficient network energy optimization of internet-of-things (IoTs) sensor nodes in wireless sensor networks (WSNs). Each sensor nodes in a WSN communicates with one another in varied methods to transfer the data from IoT cloud to the other virtual modes. Optimization of IoTs in sensor networks tends to handle the network energy and accuracy with the help of extremely complex clustering techniques. Henceforth, this paper has developed a novel adaptive ELM-Fuzzy system framework to handle the network energy level in an optimum way and thus achieving better data accuracy and network throughput by reducing the overhead of the network to the maximum possible. Numerical simulations carried out proves the consistency and efficacy of the modeled novel adaptive ELM-Fuzzy system framework for IoT based sensor network on comparing with traditional techniques from previous literatures.