This book examines the convergence of machine learning and the Internet of Things, presenting it as a transformative integration that enables intelligent, data-driven environments. It considers how connected devices generate continuous streams of data that can be analysed through learning algorithms to support automation, prediction, and adaptive decision-making. The narrative reflects on how intelligent models are embedded within networked systems, allowing devices to operate with greater autonomy and responsiveness. It also explores how computational techniques enhance the efficiency of large-scale interconnected systems, where communication, processing, and data interpretation must function seamlessly. Attention is given to the role of modelling, identification technologies, and distributed computing approaches in shaping scalable and reliable IoT ecosystems.

The book also highlights the broader significance of combining machine learning with interconnected technologies, particularly in industrial and real-world applications. It offers insight into how intelligent systems contribute to operational efficiency, innovation, and system optimisation, making it valuable for students, researchers, and professionals interested in modern data-driven technologies and connected systems.