Wireless Sensor Networks are instrumental in various domains, including environmental monitoring, healthcare, and industrial automation. Despite their ubiquity, the energy constraints of sensor nodes remain a pivotal concern. This research delves into energy conservation in WSNs by implementing Adaptive Sleep Scheduling using the Grey Wolf Optimizer (GWO) in the context of heterogeneous networks. Heterogeneous WSNs, characterized by nodes with diverse computational capacities and energy reservoirs, pose unique challenges for optimizing energy efficiency. The conventional static sleep scheduling strategies prove inadequate in accommodating this heterogeneity, often leading to suboptimal energy consumption. In this context, the application of GWO, an optimization algorithm inspired by the social behavior of grey wolves, offers a novel and promising approach to address the energy efficiency challenge. Heterogeneous WSNs necessitate tailored energy management strategies to optimize the utilization of resources. The disparity in the capabilities of sensor nodes, in terms of processing power and energy reserves, demands an adaptive solution for sleep scheduling to minimize energy consumption while maintaining the necessary data collection and transmission capabilities. This research demonstrates the significance of integrating GWO-based adaptive sleep scheduling in heterogeneous WSNs through extensive simulations and in-depth analysis. The proposed approach is compared with the LEACH, E-LEACH, and DEEC. The results show due to the use of GWO in adaptive sleep mechanisms in Heterogenous WSNs nodes die late as compared to other algorithms and energy consumption is also slow. So, in all one can say, GWO is a better approach in terms of energy efficiency and lifetime. quantity demand and investment in research and development, while the other model focuses on a more realistic relationship between the quantity demand and the price.