AI, Machine Learning, and IoT: Driving Intelligent Interconnectivity

Abstract

Situations in which computers can learn through AI, ML, and IoT have improved inter-connected systems' efficiency, making real-time and high-level decision-making and working possible. This work explores how AI and ML facilitate intelligent connectivity within IoT systems, optimize information processing, and advance real-time analytics and self-learning automation. Through the case analysis of real-world IoT systems and with the help of analytically advanced AI-ML frameworks, the research assesses the effectiveness of AI-ML integration AI-ML for IoT functions based on industry-specific applications, including healthcare, smart cities, and the manufacturing industry. The study findings show that efficient, intelligent systems enable organizations to respond between 30-35% faster and operationally efficient between 20-25% more, indicating significant gains and efficiencies that may be achieved in meeting emerging challenges. The work also discusses the limitations of developing these technologies, such as privacy issues and implementation challenges, and provides potential directions for improvement. These suggestions prove ideas of how implementing AI-ML-enabled IoT systems transforms efficiency and innovation in the digital age.