We investigated the fusion of the Intelligent Internet of Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in depression identification, its correlation with stress and anxiety, the impact of machine learning (ML) and deep learning (DL) on depressive disorders, and the challenges and potential prospects of integrating depression management with IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) and Internet of Things (IoT) paradigms to expand depression studies, highlighting data science modeling’s practical application for intelligent service delivery in real‐world settings, emphasizing the benefits of data science within IoT. Furthermore, it outlines an IIoMT architecture for gathering, analyzing, and preempting depressive disorders, employing advanced analytics to enhance application intelligence. The study also identifies current challenges, future research trajectories, and potential solutions within this domain, contributing to the scientific understanding and application of IIoMT in depression management. It evaluates 168 closely related articles from various databases, including Web of Science (WoS) and Google Scholar, after the rejection of repeated articles and books. The research shows that there is 48% growth in research articles, mainly focusing on symptoms, detection, and classification. Similarly, most research is being conducted in the United States of America, and the trend is increasing in other countries around the globe. These results suggest the essence of automated detection, monitoring, and suggestions for handling depression.

The human brain’s computer‐assisted prognosis (CAP) system relies heavily on the self‐regulating characterization of tumors. Despite being extensively researched, the classification of brain tumors into meningioma, glioma, and pituitary types using magnetic resonance (MR) images presents significant challenges. Although biopsies are currently the gold standard for evaluating tumors, the need for noninvasive and accurate methods to grade brain tumors is increasing due to the risks associated with invasive biopsies. The objective is to introduce a noninvasive brain tumor grading system based on MR imaging (MRI) and deep learning (DL) utilizing probabilistic selection techniques. In the proposed method, the best three of the seven state‐of‐the‐art deep convolutional networks are chosen after extensive experimentation and combined with a probabilistic selection technique to enhance the overall performance of the proposed classification model. The results elucidate that the proposed model successfully classifies the tumor types into Glioma, Meningioma, and Pituitary achieving a sensitivity of 0.928, 0.939, and 0.992, respectively for each tumor type. Also, the precision in classifying the tumor classes is attained as 0.969, 0.932, and 0.957, respectively claiming an accuracy of 0.966, 0.956, and 0.983 for each of the three classes. The proposed model achieved an overall classification accuracy of 96.06%, surpassing the state‐of‐the‐art advanced and sophisticated techniques. Extensive experiments were performed on brain MRI datasets to demonstrate the enhanced performance of the proposed approach. The suggested probabilistic selection technique yielded promising classification results for brain tumors and exhibited the potential to leverage the strengths of various models.

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.