The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. The traditional healthcare system, centered around hospitals and clinics, has proven inadequate in the face of such challenges. Intelligent wearable devices, a key part of conventional healthcare, leverage Internet of Things (IoT) technology to collect extensive data related to the environment, as well as psychological, behavioral, and physical health. Managing the substantial data generated by these wearables and other IoT devices in healthcare poses a significant challenge, potentially impeding decision-making processes. Recent interest has grown in applying data analytics for extracting information, gaining insights, and making predictions. Additionally, machine learning (ML), known for addressing various networking challenges, has seen increased implementation to enhance IoT systems in healthcare. This chapter focuses exclusively on exploring the hurdles encountered when integrating ML methods into the IoT healthcare sector. We offer a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios: IoT-based, ML-based, and the implementation of ML methodologies in the healthcare industry via the IoT. We highlight the difficulties faced by existing methodologies, providing valuable insights for future researchers, healthcare professionals, and government agencies. This ensures they stay updated on the latest developments in big data analytics for intelligent healthcare utilizing ML.

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