This study aims to optimize blood donor forecasting by analyzing data collected from a public questionnaire. The research examines key characteristics of blood donors using a combination of machine learning models to improve the prediction accuracy of donor availability and trends. In addition to advanced data analysis techniques, the study places a strong emphasis on data visualization to enhance understanding and accessibility. By employing interactive dashboards and graphical representations, the study reveals important trends, patterns, and relationships between various donor characteristics. These visual tools not only make the information easier to interpret but also facilitate more informed decision-making processes in blood supply management. The integration of machine learning and visual analytics provides a comprehensive approach to understanding donor behaviors, ultimately contributing to more effective planning for blood donation needs.