Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.