Wireless sensor networks (WSNs) have been employed in a number of applications with large-scale automation where human involvement might be lethal or impractical. The limiting of the network lifespan and system delay is one of the biggest problems that wireless sensor networks encounter. Since wireless sensor networks are often battery-powered, their energy usage is limited. Lesser network lifespan and more frequent system halts to refuel depleted power sources would result from increased energy usage. As a result, routing methods that use less energy are constantly sought for. The minimizing of latency is a crucial component of wireless sensor network architecture. The mechanism loses sensitivity the larger the delay or latency. When it comes to time-sensitive applications, this might prove to be quite important. In the proposed work, the clustering is determined dynamically for each iteration by applying particle swarm optimization to minimize the inter and intra cluster distances. The capacity to tackle complicated multivariate problems with strict bound limiting criteria and no single solution is the heart of the particle swarm optimization method. It is also well known that the redundant nature of the data detected and broadcast by WSNs can result in duplicate data transfers, which consume a lot of energy. By choosing and establishing a cap or threshold of the felt values, this is reduced to a minimum. Retransmissions are not carried out until the cap or threshold is exceeded. Based on network factors including the system's energy consumption, one hop, and network delay, the suggested strategy based on a PSO-threshold optimization is assessed. It has been established that, in terms of the intended wireless sensor network's performance assessment parameters, the suggested system performs better than the prevailing approach.