This new book focuses on mathematical and numerical methods for medical images and data. The book presents the various mathematical modeling techniques, numerical analysis, computing and computational techniques, and applications of machine learning for medical images and medical informatics. It also focuses on programming concepts using MATLAB and Python for medical image and signal analytics. The volume demonstrates the use of computational techniques and tools such as Machine Learning, deep neural networks, Artificial Intelligence (AI) and human-computer interaction ,fusion methods for CT and pet images, etc., for diagnosis of brain disorders, cervical cancer, lung disease, melanoma, atrial fibrillation and other circulatory issues, dental images, diabetes, and other medical issues. For further simplification and optimization, emerging techniques such as neural networks, Machine Learning, Deep Learning (DL), and Artificial Intelligence (AI) are adapted for feature extraction of medical images, medical image segmen­tation, and image-based bio-models. Image categorization, object identifica­tion, pattern recognition, and reasoning are just a few of the principles that are examined in medical imaging. By extracting meaningful patterns for a specific condition, they are employed in medical imaging to improve accu­racy. They also aid in the process of deciding on a course of action. Using Machine Learning to improve medical imaging is a trend that will continue in the future.