This chapter explores AI-empowered semantic communication, a transformative approach that shifts focus from data accuracy to meaning extraction and interpretation, enabling IoT devices to communicate with intelligence and context awareness. By integrating machine learning with semantic communication principles, IoT networks can minimize redundancy, enhance energy efficiency, and improve decision-making capabilities. The chapter begins by introducing the historical foundation of semantic communication, distinguishing it from technical communication through Weaver’s three levels: technical, semantic, and effectiveness problems. It then explores the two fundamental challenges of semantic communication: the language exploitation problem, which focuses on minimizing misinterpretation under a predefined semantic language, and the language design problem, which aims to create optimized encoding and decoding schemes to maximize communication efficiency. The deep joint source-channel coding (DeepJSCC) framework is introduced as an AI-driven technique to improve transmission quality while preserving semantic integrity. By bridging AI and semantic communication, this chapter lays a foundation for next-generation IoT networks that prioritize meaning over raw data, facilitating seamless and intelligent machine-to-machine interactions.