A review of Artificial Intelligence techniques for intelligent monitoring, disease diagnosis and smart system applications

Artificial Intelligence (AI) and Internet of Things (IoT) technologies are playing a crucial role in the development of intelligent monitoring and decision-support systems across multiple domains. These technologies enable automated data collection, real-time analysis, and rapid response mechanisms for improving safety, efficiency, and decision-making in complex environments. Recent research has demonstrated the successful application of AI techniques across smart infrastructure monitoring, healthcare diagnostics, education systems, and industrial predictive maintenance. IoT-based monitoring frameworks have been proposed to enhance urban safety by detecting hazardous environmental conditions, while deep learning models have shown promising results in medical image analysis for early disease detection, including pneumonia, cardiovascular disorders, and various cancers. In addition, machine learning and deep learning approaches have been widely applied to analyse ECG signals, medical imaging data, and biomedical signals for accurate disease diagnosis. AI-driven educational platforms and recommender systems have also been developed to improve student learning outcomes and employability. Furthermore, predictive maintenance systems using advanced deep learning models have been applied in industrial applications to monitor equipment health and predict failures. Recent studies also explore advanced computational models in fluid dynamics, renewable energy systems, and nanofluid heat transfer analysis. This review provides a comprehensive overview of recent advancements in AI-driven intelligent systems and highlights their growing impact across engineering, healthcare, and smart technology applications.