Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in training the numerical dataset. VGG was preferably chosen as a hidden layer as it has a more diverse, deeper, and simpler architecture with better performance when dealing with larger datasets. It consumes less memory and training time. A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net. The precision was 99.9% for VGG16, 96.6% for VGG19, and 100% for Alex Net; the recall values were 99.9% for all three cases of VGG16, VGG19, and Alex Net; and the sensitivity metric was 96.8% for VGG16, 97.9% for VGG19, and 98.7% for Alex Net, which has outperformed when compared with the existing approaches for the classification of Alzheimer’s disease. This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field.

Integrating Artificial Intelligence and the Internet of Things into wellness is poised to bring significant advancements, transforming patient care and physical management. AI in medical imaging and diagnosis enables faster and more accurate study of radiology like X-rays, CT scans, and MRIs. The innovation lies in personalized medicine, where AI can analyze huge information collected from IoT devices, lifestyle choices, and real-time health metrics, enabling it to cater to certain needs of individual patients, leading to improved outcomes. Expansion of off-site monitoring through IoT gadgets like fabric electronic sensors allow accurate health monitoring, enabling early detection of possible well-being ailments and timely medical medication. Objectives of the integration are predictive analytics and preventive care, enhancing chronic disease management, optimizing operational efficiency in healthcare facilities, expanding telemedicine and virtual care, addressing ethical and regulatory considerations, and accelerating AI-driven drug development and clinical trials.

Wearable health technology has evolved to incorporate a variety of technologies, including health solutions that enable personalized and personalized care. Once limited to activity tracking, wearable devices have brought technological advances that can identify health issues, monitor ongoing conditions, and help people manage stress while improving their mental health. Challenges associated with integrating with advanced technology include protecting personal data, maximizing product accuracy, interoperability across systems, and supporting a tight commitment to customer compliance. Many applications include cardiovascular monitoring, diabetes management, lung health, neurological assessment, and health monitoring, supporting preventive care, detecting abnormalities for early diagnosis, and helping individuals manage chronic diseases by providing real-time health information and insights. Make health decisions and support self-management of health.