In the ever-changing landscape of social media, achieving effective user engagement stands as a crucial factor for platform success. This research introduces an innovative approach to enhance user engagement by deploying advanced classification algorithms for tailoring content delivery. Unlike conventional methods that often rely on generic strategies, our proposed framework recognizes and addresses the diverse preferences of individual users. Through the utilization of classification algorithms, the system analyzes user behavior, preferences, and interactions, facilitating the generation of personalized content recommendations. By integrating machine learning techniques, the system dynamically adjusts to each user’s evolving interests, creating a more personalized and engaging social media experience. The study sheds light on the implementation and effectiveness of these algorithms, highlighting their potential impact on user satisfaction and platform performance. Our findings indicate that the personalized user engagement model not only improves content discoverability but also fortifies user-platform relationships, fostering a more vibrant and interactive social media environment. Remarkably, a simple support vector machine with four features achieved 99% accuracy, surpassing both the random forest model and particle swarm optimization by a significant margin. As social media platforms continue to evolve, this research contributes to the ongoing discussion on optimizing user engagement strategies. It underscores the importance of personalized content delivery in creating a more user-centric and engaging online experience.