According to statistics, 90% of the houses have computer and of them 80% have a high-speed internet connection. It is undeniable that kids use computers for a variety of purposes, including reading, typing, learning, doing their schoolwork, and playing video games. Game addiction is a serious issue connected to computer use. We are all aware of the joy, thus, the side effects gaming brings. The goal of the paper is to identify the aspects that lead to game addiction, analyze the data, and build a model to detect game addiction in youngsters. Furthermore, we are designing features to prevent the aforementioned issue. We aim to do so by using Haar Cascade Classifier (HCC), a trending technique. We make use of HCC model for detecting the users face. After successfully detecting the user, our system starts tracking the activity and sends the report to their parents at the end of the day, thus informing the parents about their child’s activity on the device. Our system is also able to nudge the user if he/she is too close to the screen, hence preventing any health-related problems. This technique made use of time-based behavior that was gathered through a user’s interactions with a laptop while playing games. Additionally, it will reduce mistakes brought on by parents who are unable to constantly watch over their children making improper behavior observations. We have successfully deployed the model as a Chrome extension. The overall purpose of this research is to detect game addiction by taking the screen time and user activity into consideration. Currently, such systems are available only on smartphones but we have developed a system for computer devices as most of the games are played on computers.