Providing a rigorous technical foundation, this chapter details the core concepts of AI and haptics. It explores various machine learning paradigms, including supervised, unsupervised, and reinforcement learning, with a specific focus on deep learning architectures like CNNs, LSTMs, and Transformers. On the haptic side, the chapter examines the biophysics of touch, the engineering of the haptic rendering loop, and the hardware backbone (sensors and actuators). It bridges these fields by discussing the AI algorithms specifically designed for processing haptic data streams to enable predictive rendering and signal classification.