<p>Improving user interaction and information retrieval is crucial for social content in all of its forms. This work combines evolutionary optimization approaches with machine learning algorithms to propose a novel approach for automatically proposing and optimizing tags on popular social media content segments. This protects against potential threats to personal information and user attention diversion. Activities include user-reported negative content marked with custom red tags, automated form feedback solicitation, accurate identification and processing, and accuracy validation. The procedure begins with a starting set of tags produced by natural language processing methods, which are then refined using a genetic algorithm. Tagging accuracy is significantly increased by this iterative approach, which improves tag selection based on content relevance. To further refine tag recommendations relying on past data, a machine learning model examines user activity patterns and preferences concurrently using Empirical Risk Minimization with Statistical analysis. The hybrid approach's adaptability and learning abilities outperform standard tagging systems by dynamically modifying tag recommendations to reflect new trends and user preferences. Moreover, integrated user input methods continuously improve and customize the hybrid optimization model. Currently, owners of web platforms promote user interaction by removing sections of content that are false. This method provides a scalable and efficient way to improve information discoverability and relevance in digital environment as social media content volume increases.</p>