Health Data Analytics (HDA) has emerged as a critical domain for extracting actionable knowledge from rapidly growing healthcare datasets. The integration of machine learning (ML) techniques with health data science has significantly enhanced disease prediction, medical image analysis, patient monitoring, and clinical decision support systems. This research and review paper examines intelligent machine learning frameworks employed in health data analytics, emphasizing their theoretical foundations, applications, emerging trends, and future research opportunities. A comprehensive synthesis of existing studies is conducted to evaluate supervised learning, unsupervised learning, ensemble methods, deep learning architectures, and data-driven analytical models in healthcare environments. The review identifies major challenges related to data quality, interpretability, scalability, privacy, and model generalization. Furthermore, a conceptual framework is proposed to guide future research in intelligent healthcare analytics. The findings indicate that machine learning-driven health data systems can significantly improve predictive accuracy and healthcare efficiency, although challenges concerning transparency, ethical deployment, and real-time implementation remain unresolved.