In a wireless sensor network (WSN), there are sets of autonomous sensor nodes distributed spatially that use wireless communication to track and document physical or environmental aspects. The series of sensor nodes (SN) cannot be changed when it is installed in an isolated or unattended region due to their wireless nature. Because of the high energy restrictions of wireless sensor devices, it is crucial to carefully manage extreme energy consumption by malevolent nodes in order to enhance network performance. To overcome this challenge fuzzy based adaptive cat swarm optimization for routing (FAR) has been proposed to decrease latency, increase network lives, and minimize energy consumption by reducing the network's energy consumption. There are two stages to the proposed FAR approach. In the first stage, the choice of the cluster head is made depending on things like energy, distance, and transmission cost using fuzzy logic. In the second stage, an adaptive cat swarm optimization method is used to choose the most efficient route for packet routing to maximize node lifetime and to ensure efficient packet routing. The effectiveness of the proposed FAR strategy has been established using metrics like packet delivery, lifetime, and energy efficiency for evaluation. According to the experimental findings, the suggested FAR model consumes less energy than the current FCEEC (fully connected energy-efficient clustering), HSA-CSO (harmony search algorithm and competitive swarm optimisation), and EECHIGWO (energy-efficient cluster head selection using a grey wolf optimization algorithm) models by 43.4%, 32.5%, and 24.1% respectively.