This project report presents a comprehensive analysis of a real-time face detection and alert system using OpenCV and Python. The system employs a pre-trained Haar Cascade classifier to detect human faces from live webcam feeds. This solution is designed to provide continuous monitoring and immediate notifications when a face is detected, ensuring enhanced situational awareness. The report details the core components of the project, including video capture, grayscale image conversion for efficient processing, and real-time face detection. When a face is identified, an alert message is generated, and the system draws bounding rectangles on the detected faces. The interface displays video output with marked detections and logs timestamps for each detection, adding a layer of traceability. The code structure emphasizes robust error handling and smooth user interaction, ensuring seamless operation. This system demonstrates practical applications in surveillance and automated monitoring, proving its potential in enhancing security measures through prompt alerts and data collection. The project highlights opportunities for future improvements, such as incorporating more advanced machine learning models for enhanced detection accuracy and integrating cloud-based storage for recording detection logs. Overall, the system's adaptability makes it suitable for both personal and professional security needs, positioning it as a practical and effective solution for modern surveillance.