The rapid expansion of the Internet of Things and the exponential rise in connected smart devices have created a paradigm shift in data management and computational intelligence. Traditional cloud-based frameworks, while powerful, often struggle with latency, bandwidth limitations, and privacy concerns when processing the vast amounts of real-time data generated by sensors and smart environments. Edge Artificial Intelligence, commonly known as Edge AI, addresses these limitations by bringing computational capabilities closer to the data source. The fusion of edge computing and artificial intelligence enables devices to make intelligent decisions locally, enhancing efficiency and responsiveness. This research paper explores the evolving role of Edge AI in real-time data processing within smart devices, analyzing its architectures, algorithms, and industrial applications. The study highlights how edge-based neural networks, lightweight machine learning models, and distributed intelligence frameworks are transforming industries like healthcare, manufacturing, transportation, and consumer electronics. The paper further discusses latency reduction, energy optimization, and data privacy enhancement as critical performance metrics of Edge AI deployments. Keywords such as edge computing, real-time analytics, machine learning inference, Internet of Things, and federated learning underscore the multidimensional potential of this technology. By synthesizing theoretical and empirical insights, this paper establishes that Edge AI not only complements cloud systems but also enables autonomous, resilient, and sustainable digital ecosystems. The exponential growth of the Internet of Things has revolutionized how data is generated, transmitted, and processed across billions of interconnected smart devices. As digital ecosystems continue to expand, the demand for instantaneous data interpretation and low-latency decision-making has become critical in modern industries. Traditional cloud-based architectures,