This paper focuses on real-time anomaly detection in surveillance video using YOLOv8, the latest in the YOLO object detection series, integrated with spatio-temporal analysis. The system aims to detect abnormal behavior in crowded environments by combining spatial object detection with temporal activity analysis. YOLOv8 is used to detect and track individuals in video frames, while a 3D Convolutional Neural Network (3D CNN) processes sequences of frames to identify behavioral anomalies based on movement patterns. Three variants of YOLOv8—Nano (n), Small (s), and Medium (m)—are evaluated for performance trade-offs in accuracy, processing speed (FPS), and latency. Results show YOLOv8n offers the best real-time performance, while YOLOv8m provides higher accuracy at the cost of increased latency. The system uses the UCF-Crime dataset for training and testing, and metrics such as accuracy, FPS, and latency are used for evaluation. The modular pipeline supports scalability and real-time deployment, with visual outputs aiding interpretation. By integrating object detection with spatio-temporal modelling, the system effectively identifies anomalies such as loitering or sudden movements. Future work includes refining detection accuracy using labelled anomalies and exploring advanced models like Transformers for improved temporal understanding. The significance of this research lies in its ability to combine lightweight real-time object detection with effective temporal behavior modeling within a scalable and modular architecture. The proposed framework contributes to the advancement of intelligent surveillance systems by improving anomaly detection reliability while maintaining computational efficiency suitable for deployment in smart cities, public safety monitoring, and edge-based surveillance applications.