In today’s digital world, harmful or offensive online information can severely impact personal and professional reputations. Traditional filtering algorithms inadequately address contextual nuances, data processing urgency, and information variety. This paper presents an innovative AI framework integrating live trend tracking, authority-based content upgrades, and dynamic tag evaluation. Using advanced natural language processing and visual content analysis, the system classifies and assesses textual and multimedia content risk levels in real time. Experimental results demonstrate improved accuracy, flexibility, and predictive capacity in identifying both immediate and long-term content-related risks.