Machine learning (ML) has become a pivotal tool that is used to transform healthcare, and drive innovations not only in disease diagnosis and treatment but also in advancing social well-being. This chapter explores how ML technologies are revolutionizing healthcare systems, focusing on their ability to enhance medical decision-making and address social determinants of health, while also improving patient care outcomes. Deep learning techniques such as CNNs and ViTs, employed in medical imaging and diagnosis, are examined to showcase their applicability to radiology and pathology. The chapter also highlights the role of reinforcement learning (RL) in personalizing treatment plans and optimizing healthcare delivery, reducing disparities in patient care. Unsupervised learning and dimensionality reduction methods are examined for their contributions to genomic analysis, disease clustering, and drug discovery, with a focus on their potential to broaden access to cutting-edge treatments. Emerging trends such as explainable AI (XAI) and generative adversarial networks (GANs) are reviewed to illustrate that they can be used ethically, to enhance data quality, and foster transparency in healthcare, contributing to solving ethics problems relating to data privacy, bias in ML models, and equitable healthcare access. The chapter underscores the importance of AI in advancing both clinical practice and social well-being. By exploring the convergence of ML with healthcare transformation, an emphasis is laid on the potentials of Machine Learning usage in healthcare for greater societal impact.