The combination of Machine Learning (ML) and the Internet of Things (IoT) is changing today’s technological and social landscape. They are spurring innovation in various sectors, and opening up revolutionary applications in manufacturing, smart cities, healthcare, agriculture, and other fields of social community. By enabling smooth connectivity between systems, sensors, and devices, the IoT produces enormous amounts of real-time data. This data is still underutilized without efficient analysis and decision-making tools. This chapter describes the capacity to identify trends, learn from data, and generate insightful predictions. ML is a vital tool for deriving useful insights from IoT ecosystems. This chapter examines how ML and IoT work together, emphasizing their integration, difficulties, and practical uses for the benefit of society. The processing of sensor data by ML-based IoT systems enables real-time responses and predictive decision-making by combining edge computing, cloud-based analytics, and distributed ML algorithms. Wearable IoT devices with ML algorithms for healthcare applications track patients’ vital signs, anticipate possible health issues, and offer prompt interventions. In order to analyse environmental data, maximize resource use, and improve crop yields, IoT-enabled smart farming employs ML. Likewise, smart cities use IoT sensors and ML to improve public safety, energy efficiency, and traffic control. The contribution of Artificial Intelligence (AI) to improving IoT security in society is also examined in this chapter. The intricacy of IoT data streams is being addressed by this chapter with advanced ML techniques like deep learning, which provide increased accuracy in predictive tasks like fault detection, energy forecasting, and autonomous system control.