TinyML refers to the deployment of machine learning models on small, resource-constrained devices such as microcontrollers and embedded systems. It enables real-time data processing and analytics locally, reducing latency and reliance on internet connectivity. TinyML forms the backbone of edge AI, facilitating applications like smart sensors, wearables, and IoT devices with limited power, memory, and computational capacity.