This chapter highlights the profound impact of machine learning (ML) on the field of medical imaging. Medical imaging is vital to modern healthcare, providing clinicians with vital visual insights for diagnosis and treatment planning without invasive procedures. The integration of ML algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), support vector machines (SVMs), and decision trees/random forests, is fundamentally transforming how imaging data is analysed and utilized. ML enhances diagnostic accuracy, enables personalized treatment strategies, and streamlines workflow efficiency in medical imaging applications. Despite significant progress, challenges including data requirements, interpretability, and ethical considerations must be addressed through interdisciplinary collaboration. Future efforts focusing on data acquisition, privacy preservation, and regulatory standards will drive transformative advancements in ML-driven medical imaging, ultimately enhancing healthcare delivery and improving patient outcomes across mortality, safety, patient experience, effectiveness, and patient satisfaction.