This study presents a new intrusion detection system (IDS) for Wireless Ad hoc Networks, leveraging graph neural networks (GNN). Overcoming the challenges faced by traditional IDS in dynamic environments, our approach focuses on meticulous feature engineering and normalization for optimal efficiency. The proposed GNN based IDS, strategically placed at zone heads, effectively filters malicious packets while considering the limited resources of wireless devices. Evaluation on the NSL-KDD dataset demonstrates the GNN-based IDS's superiority over other models like Convolutional Neural Networks (CNNs) and Transformers, highlighting its adaptability to dynamic network environments. The GNN-based IDS excels in understanding complex dependencies, contributing to efficient intrusion detection and network security in resource-constrained Wireless Ad hoc environments. Emphasizing resource efficiency, our GNN-based approach proves practical for real-time intrusion detection in resource-constrained Wireless Ad hoc networks. This work not only emphasizes GNNs' transformative role in enhancing Wireless Ad hoc network security but also contributes to discussions on efficient intrusion detection, offering innovative solutions against emerging threats.