With increasing security threats in public spaces, automatic detection of dangerous weapons and fire accidents in surveillance videos is an important capability for pre-emptive safety measures. Anomaly detection in computer vision has become an important research problem with applications in surveillance, industrial inspection, and medical imaging. Recent advances in deep learning provide powerful techniques for analysing visual data and identifying irregularities. The proposed system uses edge detection to isolate potential hazards in video frames, followed by a convolutional neural networks (CNN) classifier and other techniques to identify anomalies such as weapons, fire, and violence in the cropped image patches, and thus provides a survey of deep learning techniques for anomaly detection in CCTV surveillance videos. A key advantage of deep learning in video analysis is its ability to learn feature representations directly from visual data and CNN can capture spatial patterns, while recurrent networks can model temporal dynamics in video. Techniques including pre-trained models such as feature extractors, combining convolutional and recurrent architectures, generating region proposals to focus on anomalous areas, and training models on labelled anomaly data are used. However, challenges remain, including limited training data, difficulty modelling all normal patterns, and reducing false alarm rates. Deep CNNs are well-suited for weapon detection across images and videos, capturing visual features associated with firearms and violent intent. The experimental results validate the proposed anomaly detection system, demonstrating real-time efficiency and impressive accuracy in tasks like violence (91%), weapon (98%), and fire (95%) detection.