1Mr.Jajjara Bhargav, 2Doppalapudi Satya Sai, 3Patti Venkat Gopiram, 4Kakunuri Gopaiah, 5RANGISETTI SAI SUJITHA, 6Anitha 03 04 The increasing demand for intelligent surveillance systems has led to the development of automated solutions capable of detecting abnormal activities in real time Traditional CCTV monitoring systems rely heavily on human operators making them inefficient timeconsuming and prone to errors due to fatigue and limited attention span This research proposes a RealTime Anomaly Detection System in CCTV using Machine Learning and Computer Vision techniques to overcome the limitations of manual monitoring The system is designed to automatically analyze video streams identify unusual activities and generate alerts without human intervention The proposed system captures live video footage from CCTV cameras and processes it into frames using OpenCV These frames are analyzed using machine learning algorithms that compare realtime activity with predefined normal behavior patterns Any deviation from normal behavior is classified as an anomaly The system is capable of detecting various abnormal events such as unauthorized entry suspicious movements theft and violent activities Upon detection an alert notification is generated and stored along with timestamp details in a SQLite database A webbased dashboard developed using Flask allows users to monitor alerts and system performance efficiently The system architecture includes modules such as video capture frame processing feature extraction anomaly detection alert generation and database management The integration of these modules ensures realtime performance and high detection accuracy Experimental results demonstrate that the system achieves significant accuracy while maintaining a low false alarm rate and fast processing speed The proposed system reduces the burden on human operators and enhances surveillance efficiency in public places such as airports banks railway stations and shopping malls It provides a scalable and costeffective solution for modern security challenges Future enhancements may include deep learningbased models cloud deployment and integration with IoT devices for improved performance Overall this research contributes to the advancement of intelligent surveillance systems by providing a robust and automated anomaly detection framework Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026