The Internet of Things (IoT) has become a transformative force across various sectors, including healthcare, offering new opportunities for automation and enhanced service delivery. The evolving architecture of the IoT presents significant challenges in establishing a comprehensive cyber‐physical framework. This paper reviews recent advancements in IoT‐driven healthcare automation, focussing on integrating technologies such as cloud computing, augmented reality and wearable devices. This work examines the IoT network architectures and platforms that support healthcare applications while addressing critical security and privacy issues, including specific threat models, attack classifications and security prerequisites relevant to the healthcare sector. This study highlights how emerging technologies like distributed intelligence, big data analytics and wearable devices are incorporated into healthcare to improve patient care and streamline medical operations. The findings reveal significant potential for IoT to transform healthcare practices, particularly in‐patient monitoring, and clinical decision‐making. However, security and privacy concerns continue to be a substantial barrier. The paper also explores the implications of global IoT and ehealth strategies and their influence on sustainable economic and community growth. It proposes an innovative cooperative security model to mitigate security risks in IoT‐enabled healthcare systems. Finally, it identifies key unresolved challenges and opportunities for future research in IoT‐based healthcare.

The liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol‐based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning‐based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement.