Wireless sensor networks (WSNs) play a vital role in sensing environmental conditions in far-flung areas. However, their energy consumption remains a critical issue, affecting the network’s lifetime and coverage area. Clustering has emerged as an efficient strategy to prolong sensor network lifespan, and the Fruit Fly Algorithm (FFA) and Ant Colony Optimization (ACO) are promising techniques for cluster formation and efficient path establishment, respectively. In this study, we propose an innovative approach that combines FFA for cluster formation and ACO for path establishment. This novel algorithm is implemented in MATLAB and evaluated in both homogeneous and heterogeneous environments. We compare our proposed algorithm with the Biogeography-Based Optimization Algorithm (BOA) and the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Our results indicate that the proposed algorithm significantly outperforms both BOA and LEACH in terms of network lifetime and coverage area, particularly in heterogeneous environments.