This study explores how integrating predictive analytics into contemporary healthcare management improves decision-making and patient outcomes. A mixed-methods approach, including surveys and interviews, examines how demographic factors like age, gender, occupation, and ethical familiarity influence the adoption of predictive analytics in healthcare. Drawing from decision theory and behavioral economics, the research delves into the cognitive processes shaping health care decision-making. By analyzing perspectives across diverse demographics, it uncovers correlations between demographics, predictive analytics use, and healthcare outcomes. The research offers insights for optimizing resource allocation, refining patient care, and fostering informed decision-making. It contributes to theoretical understandings of health care decision-making and provides actionable recommendations for evidencebased management practices. Ultimately, this research aims to drive continuous improvements in patient outcomes and advance global healthcare systems.