This document is a comprehensive survey article presenting a thorough review of artificial intelligence driven channel state information extrapolation techniques for sixth generation wireless communication systems. The paper systematically analyzes CSI extrapolation across four domains, namely time domain, frequency domain, antenna domain, and multi domain, covering both model driven and AI driven approaches including multilayer perceptrons, recurrent neural networks, long short term memory networks, gated recurrent units, convolutional neural networks, graph neural networks, and Transformer based architectures. Performance metrics specific to 6G CSI extrapolation are discussed, encompassing extrapolation accuracy, generalization to dynamic scenarios, and computational complexity. The survey reviews state of the art techniques addressing key 6G challenges such as extremely large scale MIMO spatial nonstationarity, high mobility Doppler effects exceeding 1000 km per hour, frequency division duplex partial channel reciprocity, and multi band propagation across sub 6 GHz, millimeter wave, and terahertz bands. Open source wireless channel datasets including DICHASUS, DeepMIMO, WARI D, and DataAI 6G are evaluated alongside channel simulators such as QuaDRiGa, Sionna RT, and NYUSIM. The paper identifies critical research challenges including dataset scarcity, near field channel modeling, cross domain coupling, and computational constraints, and proposes future research directions covering dataset construction, generalization performance evaluation, lightweight model design, physics informed neural networks, multi modal assisted extrapolation, and integration with emerging technologies including reconfigurable intelligent surfaces, fluid antenna systems, non orthogonal multiple access, and low Earth orbit satellite communications.