In response to the increasing need for energy-efficient wireless sensor networks (WSNs) in the quickly evolving Internet of Things (IoT) arena, we introduce ReLeC-MEO, a new protocol that combines the ReLeC clustering approach with multi-objective efficient optimization. ReLeC-MEO improves energy efficiency by using clustering based on reinforcement learning to optimize network design. By finding non-dominated solutions on the Pareto front, multi-objective optimization enhances this procedure even more and guarantees a just trade-off between data transmission quality, energy consumption, and network lifetime. Numerous simulations verify that ReLeC-MEO works noticeably better than current techniques. In comparison to baseline protocols, it specifically achieves a 42.9 reduction in latency, a 51.6 drop in energy consumption, and a 35 increase in throughput. It also outperforms the next best protocol by 20.4 in terms of network longevity. © 2025 Elsevier B.V., All rights reserved.