Abstract Stampede incidents in densely populated environments remain a critical challenge for public safety, resulting in severe casualties and significant disruption. This paper presents a novel, data-driven framework for automated stampede detection and crowd risk classification, leveraging the integration of Farneback optical-flow computation with a hybrid CNN-LSTM architecture. Two complementary datasets, UCSD Anomaly detection dataset and Agoraset dataset, were combined to capture a broad spectrum of crowd behaviors and densities. The proposed system classifies crowd states into four distinct risk levels: normal, moderate, dense, and risky, thereby offering a more granular assessment than traditional binary models. The model was trained and evaluated on a balanced dataset of 10,000 annotated frames, with rigorous preprocessing and augmentation to ensure robustness. Experimental results demonstrate an accuracy of 99.75%, further cross-dataset evaluation on the UMN benchmark assesses the robustness under domain shift conditions. While the approach shows strong potential for real-time deployment in public event management and emergency response, current limitations include computational latency and challenges in ultra-dense, occluded scenarios. Similar content being viewed by others Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Cob-Parro, A.C., Losada-Gutiérrez, C., Marrón-Romera, M., Gardel-Vicente, A., Bravo-Muñoz, I. & Sarker, M.I. A proposal on stampede detection in real environments. In: Proceedings of the IPIN 2021 WiP, Lloret de Mar, Spain (2021). https://ceur-ws.org/Vol-3097/paper29.pdf Sharif, M. H. & Djeraba, C. An entropy approach for abnormal activities detection in video streams. Pattern Recogn. 45(7), 2543–2561. https://doi.org/10.1016/j.patcog.2011.11.023 (2012). Duives, D. C., Oijen, T. & Hoogendoorn, S. P. Enhancing crowd monitoring system functionality through data fusion. Sensors 20(20), 6032. https://doi.org/10.3390/s20216032 (2020). Lalit, R. & Purwar, R. Crowd abnormality detection using optical flow and glcm-based texture features. J. Inf. Techn. Res. 15 (2022) https://doi.org/10.4018/JITR.2022010110 Jadhav, C., Ramteke, R. & Somkunwar, R.