The exponential growth of digital data has transformed the business landscape, creating opportunities for organizations to derive valuable insights from large and complex datasets. Data mining has emerged as a critical component of Business Intelligence (BI), enabling enterprises to discover hidden patterns, relationships, trends, and knowledge that support strategic decision-making. Through techniques such as classification, clustering, association rule mining, regression analysis, anomaly detection, and predictive modeling, organizations can improve customer understanding, optimize operations, enhance risk management, and strengthen competitive advantage. This study examines the role of data mining techniques in business intelligence applications through a comprehensive review of academic literature and industry practices. The findings indicate that effective use of data mining significantly improves organizational performance, operational efficiency, customer satisfaction, and innovation capability. However, challenges including data quality issues, privacy concerns, technological complexity, and skills shortages remain significant barriers. The study proposes an integrated framework for implementing data mining in business intelligence systems and highlights future developments in analytics-driven decision-making.