This chapter provides a comprehensive overview of medical information processing in the era of big data, highlighting its role in advancing modern healthcare research and clinical practice. With the proliferation of diverse health-related data—such as genomic sequences, medical images, electronic health records, and clinical voice data—the medical domain has witnessed a paradigm shift toward data-driven methodologies. Traditional statistical approaches are increasingly inadequate in addressing the polymorphic, incomplete, and redundant nature of medical data. In response, advanced computational techniques—including machine learning and deep learning methods such as AdaBoost, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN)—have been widely adopted for tasks like gene analysis, image segmentation, and clinical outcome prediction. The chapter introduces foundational concepts and methodological developments in medical information processing, reviews core data resources, and outlines current analytical techniques. It also presents representative application cases, such as intelligent health promotion systems for chronic disease management, to demonstrate the integration of big data analytics with biomedical informatics.