Tiny Machine Learning (TinyML) has emerged as a significant advancement in embedded Artificial Intelligence (AI), enabling machine learning inference directly on resource-constrained microcontrollers and ultra-low-power edge devices. By integrating lightweight machine learning models with embedded systems, TinyML facilitates real-time, energy-efficient, and privacy-preserving intelligence at the edge of Internet of Things (IoT) ecosystems. This chapter presents a comprehensive introduction to TinyML, examining its evolution from conventional cloud-centric AI and Edge AI architectures toward distributed embedded intelligence. The chapter discusses the fundamental architecture of TinyML systems, key model optimization and deployment techniques, including quantization, pruning, and model compression, as well as hardware-aware design strategies for efficient on-device inference. Furthermore, major application domains such as healthcare, consumer electronics, industrial automation, agriculture, and environmental monitoring are explored to demonstrate the practical relevance of TinyML across diverse sectors. The chapter also evaluates the principal advantages and limitations of TinyML and outlines a practical development workflow encompassing hardware selection, software frameworks, data acquisition, model training, optimization, and deployment. Overall, TinyML represents a critical enabling technology for scalable, low-power, and autonomous intelligent systems, supporting the next generation of edge computing and IoT applications.