For businesses that primarily rely on data-driven decision-making, data quality assurance, or DQA, is essential. Maintaining data integrity and reliability becomes more difficult for traditional techniques of assuring data quality as data volumes and variety increase. In order to better understand how AI techniques can improve data integrity and reliability, this study examines the application of AI in proactive data quality assurance. Our proposal is a framework that utilizes natural language processing (NLP) and machine learning (ML) to identify, rectify, and avoid problems with data quality before they affect subsequent operations. We verify the efficacy of our proposed framework using a case study, demonstrating notable gains in data quality measures over conventional approaches. The results imply that AI has the potential to be an effective instrument for guaranteeing good data quality, facilitating more precise analytics and wiser decision-making.