Crowd anomaly detection is a critical research area that addresses the growing need for ensuring safety and security in densely populated urban environments. Traditional CCTV-based surveillance systems often struggle with real-time detection of suspicious activities due to challenges such as occlusions, crowded scenes, and complex human behaviors. VUEBLOX project proposes an advanced, explainable, and occlusion-aware framework for robust crowd anomaly detection. The system integrates multiple deep learning modules, including Masked Autoencoders (MAE) to handle occlusions by reconstructing partially visible objects, Graph Neural Networks (GNN) to capture intricate spatial relationships, and SimCLR for contrastive feature representation. Furthermore, the model employs an ensemble voting mechanism to aggregate outputs from different modules and improve anomaly detection accuracy. Explainability is a key focus of this framework, achieved through techniques such as heatmap visualizations and graph-based reasoning, which provide insights into the decision-making process and enhance user trust. The system was trained and evaluated using benchmark datasets like UCSD Ped1 and Ped2, demonstrating high detection accuracy and robustness under occluded scenarios. Results showed that integrating occlusion-aware modules significantly improved the model’s performance compared to conventional methods. This research contributes to the field of intelligent surveillance by offering a reliable and interpretable solution that bridges the gap between deep learning advancements and practical deployment in real-world public safety applications.