Integrating the IoT with advanced BDA holds significant potential for minimizing healthcare expenditures and proactively identifying medical risks. IoT-driven BDA transforms healthcare systems by enhancing data collection, storage, extraction, and utilization processes, thereby improving service delivery across medical disciplines. This review critically examines the individual advantages and limitations of IoT and BDA technologies, while emphasizing the necessity of their synergistic integration to address complex healthcare challenges. The author explores the role of IoT-generated heterogeneous data—including electronic health records, medical imaging, genomic datasets, and real-time sensor data—alongside BDA techniques that enable advanced clinical decision-making, personalized care, and efficient resource management. Key applications, methodologies, and challenges of IoT-enabled healthcare systems are systematically analyzed, with a focus on technical, ethical, and operational barriers such as data heterogeneity, security, and interoperability. Furthermore, the paper evaluates current research projects addressing these challenges and identifies critical gaps in existing frameworks, particularly in scalability, real-time analytics, and edge computing. Based on this analysis, the author proposes future research directions to advance IoT-BDA integration, including adaptive machine learning models, federated data architectures, and context-aware analytics. By synthesizing insights from recent literature, this survey provides a comprehensive foundation for developing robust, data-driven healthcare solutions capable of harnessing the full potential of IoT and big data technologies.