<p>To address the deployment challenges in Wireless Sensor Networks (WSNs), such as high energy consumption and limited network lifetime, a multi-strategy fusion parrot optimizer (PO-SS) was proposed. In the exploration phase, an acceleration function and a novel exploration mechanism are introduced to mitigate the randomness in node position selection, thereby enhancing global search capability. In the exploitation phase, five statistical vibration frequencies are incorporated to replace the standard Lévy flight, significantly improving the fine-tuning accuracy of the original Parrot Optimizer (PO). To validate the proposed method, simulations are conducted on both obstacle-free and obstacle-rich models. Results demonstrate that PO-SS outperforms the baseline PO. In the obstacle-free model, the coverage rate increases by 3.56%, while the waste rate and energy consumption decrease by 0.99% and 0.00028%, respectively. Similarly, in the obstacle-rich model, the coverage rate improves by 1.07% with corresponding reductions in waste and energy metrics. Furthermore, the optimized node distribution enhances network throughput and reduces end-to-end delay. Comparative experiments against four classical meta-heuristics (SSA, FPA, WOA and HBA) confirm that PO-SS achieves superior performance in complex deployment scenarios.</p>