Data science revolutionizes healthcare by enhancing diagnostic accuracy, enabling personalized treatment, and optimizing patient outcomes. By integrating advanced techniques such as machine learning (ML), artificial intelligence (AI), natural language processing (NLP), and big data analytics, researchers can analyze vast datasets from sources like electronic health records (EHRs), medical imaging, and genomics. This paper reviews the state-of-the-art healthcare data science applications, emphasizing predictive analytics, NLP for unstructured data processing, and big data for public health surveillance. Despite significant progress, data integration, model interpretability, and ethical concerns remain. A systematic literature review identifies gaps, such as the lack of standardized frameworks and the need for transparent AI models. Future research should focus on developing scalable, interpretable solutions and fostering interdisciplinary collaboration to maximize the potential of data-driven healthcare.