In the contemporary data-driven business landscape, organizations accumulate vast amounts of data through digital transactions, customer interactions, and operational processes. This research investigates the application of data mining techniques in enhancing Business Intelligence (BI) systems within IT organizations. The study employs a quantitative descriptive and analytical research design, collecting data from 100 IT professionals through a structured questionnaire of 15 items. Findings reveal that Classification (34%) and Regression Analysis (36%) are the most widely adopted and perceived as most useful data mining techniques for improving BI effectiveness. Business Intelligence is primarily used for project performance tracking (38%) and strategic decision-making (30%). Data mining contributes to BI by adding predictive insights (36%), identifying hidden patterns (32%), and improving forecast accuracy (22%). Key challenges include shortage of skilled professionals (38%) and poor data quality (30%). The study confirms  that data mining techniques significantly enhance the quality and accuracy of Business Intelligence in IT organizations, with 67% of respondents reporting moderate to significant organizational performance improvement. Furthermore, 82% of respondents strongly anticipate that AI and Machine Learning integration will further advance BI capabilities in the future. The study recommends investments in employee training, cloud-based BI platforms, data governance frameworks, and roadmaps for AI/ML integration.