The surge in data across industries has highlighted the critical importance of effective data management strategies, especially in the realm of data cleansing. While traditional data cleansing methods have been fundamental, they often struggle to keep pace with the increasing complexity and scale of modern data environments. This study investigates the use of artificial intelligence (AI) in data purification, presenting a shift towards more precise, scalable, and efficient data management solutions. By comparing conventional data cleansing techniques with AI-driven approaches, the study demonstrates the superior advantages of employing machine learning algorithms and natural language processing for maintaining data integrity. The methodology encompasses a review of recent research, an evaluation of various AI models and algorithms for data cleansing, and the presentation of case studies that showcase the practical benefits of these technologies. The findings reveal that AI-powered data cleansing offers adaptive capabilities crucial for managing dynamic data landscapes and proves to be more accurate and efficient than traditional methods. This study advances our understanding of AI's role in improving database accuracy and integrity by providing insights into future directions for integrating cutting-edge AI technology into data management practices. The implications of this research extend beyond academic interest, offering organizations actionable recommendations for enhancing data quality and achieving operational excellence through AI adoption.