This discussion explores the evolution of IoT from centralized cloud computing to a distributed architecture that incorporates Edge and Fog computing, enhancing efficiency and responsiveness by allowing for data processing and decision-making closer to the source. The traditional model of centralized cloud computing, where all data is transmitted to a central platform for processing, is contrasted with Edge Computing, which performs computations directly on IoT devices or nearby gateways, providing fast, localized responses. Fog Computing, an intermediate layer that aggregates data from multiple edge devices for more substantial local processing before communicating with the cloud, is also examined. The benefits of edge processing, including reduced latency, conserved network bandwidth, enhanced privacy and security, and increased reliability through offline capabilities, are analyzed. Edge AI (TinyML), which involves running optimized AI models on resource constrained edge devices, is delved into, enabling sophisticated perception and decision-making at the device level. The evolution to a distributed architecture is recognized as essential for the maturation of IoT, creating a synergistic partnership between the speed of the Edge, the coordination of the Fog, and the intelligence of the Cloud, unlocking new real time, mission critical applications in sectors like industrial automation and healthcare.