Abstract The Internet of Things (IoT) has become a transformative paradigm that connects billions of devices, sensors, and systems, generating large volumes of diverse data. However, managing, analyzing, and deriving actionable insights from this data remains difficult. Machine Learning (ML), with its ability to recognize patterns, predict outcomes, and adapt over time, has become a key technology for improving IoT ecosystems. This study examines the positive role of ML in IoT, focusing on applications in smart cities, healthcare, agriculture, industrial automation, and cybersecurity. The study includes limited mathematical formulas, comparative tables, and conceptual diagrams to show how ML integrates into IoT. Contributions to knowledge include demonstrating how ML facilitates predictive maintenance, anomaly detection, intelligent decision-making, and increased efficiency in IoT-based systems. Finally, the paper discusses future research directions, including federated learning, green IoT, explainable AI, and integration with nextgeneration 6G networks. Keywords: Machine Learning, Internet of Things, Smart Systems, Predictive Analytics, Cyber-Physical Systems