The rise of social media has led to an overwhelming surge in user- generated content, but much of this data is riddled with misinformation, redundancy, and inconsistencies. For businesses, researchers, and policymakers relying on real-time insights, poor data quality can result in misleading conclusions and flawed decisions. Conventional data-cleaning approaches often fail to keep up with the rapid and unpredictable nature of social media streams, creating the need for a more intelligent and adaptive solution. This paper introduces an AI- driven adaptive framework designed to enhance data quality in real-time social media environments. By incorporating machine learning, natural language processing (NLP), and reinforcement learning, the framework continuously evaluates and refines incoming data to improve its accuracy, consistency, and relevance. A real-time processing pipeline ensures seamless ingestion, filtering, and transformation of noisy data using efficient streaming architectures. Experimental results on publicly available social media datasets show that the proposed framework significantly enhances data quality while maintaining low processing latency. The findings highlight a notable improvement in data reliability and usability, surpassing traditional methods in terms of accuracy and adaptability. The framework has promising applications in areas like sentiment analysis, misinformation detection, and social media trend analysis. Moving forward, future enhancements will focus on large-scale deployment and seamless integration across multiple social media platforms to further improve its effectiveness.