Cloud computing environments increasingly face sophisticated cyber threats due to their distributed and open-access nature, demanding robust security solutions for data integrity. To address these challenges, this work proposes a Hybrid Multi-Model Ensemble System that uses advanced hypergraph techniques for comprehensive intrusion detection and prevention. The system combines models such as Hypergraph Neural Networks (HNN), Variational Autoencoders (VAE), and Graph Attention Networks (GAT) to boost detection precision for complex attack patterns. An Adversarial Module with ZOO Model (Zeroth Order Optimization) boosting generates challenging attack samples, strengthening the pipeline’s resilience against evolving threats. Real-time protection is reinforced through an SNORT-powered Intrusion Prevention Module that blocks intrusions proactively with adaptive policies. Experiments show that the proposed ensemble achieves detection accuracy above 90%, surpassing single-model approaches. This strong accuracy, along with reliable recall and precision, confirms the hybrid architecture minimizes false positives while accurately classifying diverse intrusion types. Overall, the system’s advanced modeling and adaptability offer a scalable solution for securing cloud infrastructures against known and novel attacks.