The expansion of the Internet of things (IoT) has resulted in the creation of massive volumes of data, demanding advanced analytics for real-time decision-making in smart settings. Traditional cloud-based ways to processing IoT data frequently encounter issues such as excessive latency, privacy concerns, and bandwidth constraints. Edge-based machine learning provides a transformational solution by allowing data processing to occur at the network’s edge, closer to the source of data collection. This study investigates the integration of machine learning algorithms into edge devices within IoT networks, emphasizing the advantages of this strategy for improving real-time analytics. Edge-based machine learning has several advantages, including reduced latency, greater data privacy, and increased scalability for IoT installations. Edge-based solutions reduce operating expenses and bandwidth consumption by processing data locally on devices such as sensors, gateways, and embedded systems. This localized processing also enables faster and more context-aware decision-making, which is critical for time-sensitive applications such as self-driving cars, industrial automation, and smart healthcare systems. This study also examines the technological problems and issues associated with deploying machine learning models on edge devices, such as computing restrictions, energy efficiency, and model optimization. Several use scenarios are shown, illustrating how edge-based machine learning may be used to improve the performance and reliability of smart environments such as smart cities, smart homes, and industrial IoT.