Published Paper Details:

Machine learning (ML) has emerged as a transformative force in modern healthcare, enabling computers to learn from complex medical data and assist clinicians in making faster, more accurate decisions. This paper provides a structured review of how ML techniques -- including supervised learning, unsupervised learning, reinforcement learning, and deep learning -- are being applied across key healthcare domains such as disease diagnosis, drug discovery, medical imaging, patient monitoring, and genomics. We examine representative studies from the literature, compare ML algorithms with respect to accuracy and clinical utility, and discuss the practical challenges that currently hinder large-scale adoption, including data privacy, model interpretability, and regulatory compliance. We further identify open research gaps and outline promising directions for future work, including federated learning, explainable AI, and multimodal data fusion. Our aim is to offer researchers and practitioners a clear, accessible overview of the current landscape and the road ahead.