Abstract: In the contemporary business environment, management information systems are no longer limited to the collection, storage, and presentation of historical data; rather, they increasingly constitute an intelligent foundation for anticipatory and proactive business decision-making. In this context, predictive analytics and machine learning occupy a central position in the development of modern MIS, as they enable the identification of patterns in data, the estimation of probable future outcomes, and the timely response of management. The aim of this paper is to analyze the role of predictive analytics in contemporary information systems, with particular emphasis on its integration into business processes and its impact on the quality of business decision-making. The paper examines the theoretical foundations of business intelligence, predictive analytics, and machine learning; presents the architectural framework of their integration into MIS; and analyzes typical applications in customer management, inventory management, process management, and risk management. A separate section of the paper is designed as a simulated research model that illustratively demonstrates how the effects of predictive analytics could be empirically examined in organizations. The findings indicate that the integration of analytical models into modern Management Information Systems (MIS) contributes to higher-quality decision-making, faster managerial response, and greater operational efficiency; however, the full effects depend on data quality, professional competencies, and organizational readiness for change.
Faruk Unkić* (2024) studied this question.