This chapter explores the transformative impact of AI-enabled feedback communication in IoT networks, focusing on how AI enhances traditional feedback mechanisms to optimize energy efficiency, reliability, and adaptability. Conventional IoT communication relies on limited feedback, such as ACK/NACK mechanisms, which provide only basic confirmation of message reception. In contrast, AI-driven feedback strategies leverage deep learning models to extract richer information from the receiver’s state, dynamically adjusting the coding strategy of the IoT device for improved spectral efficiency and reduced power consumption. The chapter introduces the Feedback-Enhanced IoT framework, a paradigm shift that adopts a “listen more, transmit less” philosophy, significantly extending device lifespan by minimizing redundant transmissions. Through information-theoretic foundations, we establish how feedback influences the capacity, reliability, and power efficiency of IoT communication systems, particularly in finite block-length and variable-power scenarios. The discussion extends to AI-driven coding techniques, including DeepCode, AttentionCode, and Generalized Block Attention Feedback codes. The superiority and flexibility of AI-based symbol-wise and block-wise feedback codes are demonstrated through numerical experiments in noisy and resource-constrained conditions. The chapter concludes by highlighting the broader implications of AI-powered feedback communication for next-generation IoT networks.