With the growing scale of IoT sensor deployments, edge computing has become essential for reducing computational burden, accelerating decision-making, and supporting latency-sensitive applications, such as healthcare monitoring, industrial inspection, smart agriculture, manufacturing, and environmental sensing. As IoT systems evolve toward higher levels of intelligence and autonomy, the traditional data-centric paradigm is shifting toward an integrated closed-loop framework of perception–computation–decision–execution. This transition requires advanced pattern recognition methods that can accurately interpret heterogeneous sensor data while operating under resource-constrained, low-latency, and privacy-sensitive environments. This Special Issue (SI) aims to present recent advances in intelligent pattern recognition for IoT systems, covering the full pipeline from sensing and data acquisition to edge–cloud collaborative intelligence. It welcomes original research and review articles addressing multimodal data processing, lightweight AI deployment, real-time recognition, trustworthy learning, and system-level integration for practical IoT applications.