The growing sophistication in the structure and behavior of current networked systems requires sophisticated methods that can effectively uncover and understand the complicated patterns and relationships in graph-based data. In this paper, a new hybrid approach is introduced that utilizes the combination of Graph Neural Networks (GNNs) along with conventional machine learning techniques to improve complex network intelligence. This proposed method uses Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Autoencoders (GAEs) to develop advanced node embeddings, which are useful for understanding both local and global graph structures along with using ensemble learning algorithms for decision making. It is observed through quantitative analyses on several benchmark datasets that the hybrid architecture performs better than the individual methods in terms of accuracy for node classification, anomaly detection, and influential nodes identification tasks.