A wireless sensor network (WSN) is an array of specialized transducers coupled with a communication system designed to monitor and record conditions in different locations. However, large amounts of energy consumption, less network lifetime, malicious attacks, and a limited range of batteries are the critical issues associated with WSN, which results in inappropriate routing, delay in packet arrivals and delivery, imbalanced energy conservation, and so on. Consequently, these issues cannot be resolved reliably. In this research work, a novel dragonfly-optimized K-means clustering (DFO) algorithm was proposed. Initially, the sensor nodes (SN) are initialized to enhance the lifetime of the networks and node density and consume less energy. This sensor nodes are clustered via fuzzy K-means clustering, and CH selection is done by DFO algorithm. Hence, the energy consumption, network lifetime, number of alive nodes residual energy, and throughputs are the evaluation metrics used to assess the proposed DFO approach. This scheme is simulated by using MATLAB 2019. A comparison is made between proposed DFO and existing algorithms such as DERNNet, BWO-IACO, and GSA, respectively.