  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 modern
healthcare, leverage Internet of Things technology to collect extensive data
related to the environment as well as psychological, behavioral, and physical
health. However, 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, known for addressing various big data and
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. It
offers a comprehensive summary of current research challenges and potential
opportunities, categorized into three scenarios: IoT-based, ML-based, and the
implementation of machine learning methodologies in the IoT-based healthcare
industry. This compilation will assist future researchers, healthcare
professionals, and government agencies by offering valuable insights into
recent smart healthcare advancements.
