In the context of Software-Defined Networking (SDN)-enabled Wireless Sensor Networks (WSNs), efficient load balancing is critical to minimise delay, reduce energy consumption, and enhance network performance. This paper proposes a hybrid optimisation model integrating Genetic Algorithm (GA) and Ant Colony Optimization (ACO) for achieving adaptive load balancing, GACO-LB model. The proposed model GACO-LB leverages the exploration capabilities of ACO for dynamic path selection as well as the optimisation strength of GA for global load balancing, also it shows 96% of packet delivery ratio in the environment of controllers that helps to distribute packages uniformly. The comparative simulations with baseline techniques, for instance, Round Robin and Random Walk, show the hybrid GACO-LB model to achieve superior performance in terms of reduced end-to-end delay, energy consumption, and balanced traffic distribution across the network nodes. This model helps to overcome the challenges and provides a better application to the area where humans face such challenges.