As the automation and smart technologies are quickly becoming essential for our daily life, the facial analysis systems have emerged as strong tools in most fields. They find applications in secure phone login and live monitoring for identification and personalized services. This paper deploys a real-time facial analysis system based on Python, the OpenCV library and Intel’s robust computer vision platform. The system concentrates on two important tasks: age estimation and gender determination of the persons identified from a live video stream. Employing a webcam video stream, the system takes real-time facial shots, detects human faces, and uses trained machine learning classifiers to estimate the age group and gender of each face. Leveraging pre-trained deep learning models guarantees reasonable accuracy and robustness across changing lighting and background conditions. The solution is platform-agnostic and can be deployed on both Windows and macOS, demonstrating its scalability and portability. This facial analysis technology can be used in a variety of real-world applications like retail analytics, intelligent surveillance, targeted marketing, and demographic data gathering. For example, shopping malls can profile customer demographics for improved service targeting, and companies can track environments for safety or regulatory reasons. Under good illumination, the system was about 93% accurate for gender classification and 70–65% accurate for age estimation when tested. These outcomes established the system’s capability for real-time usage and provided a basis for future growth, such as facial emotion detection and multi-person tracking.