The wireless communication systems of today rely to a large extent on the condition of the accessible channel state information (CSI) at the transmitter and receiver. Channel aging, denoting the temporal and spatial evolution of wireless communication channels, is influenced by obstructions, interference, traffic load, and user mobility. Accurate CSI estimation and prediction empower the network to proactively counteract performance degradation resulting from channel dynamics, such as channel aging, by employing network management strategies such as power allocation. Prior studies have introduced approaches aimed at preserving high-quality CSI such as temporal prediction schemes, particularly in scenarios involving high mobility and channel aging. Conventional model-based estimators and predictors have historically been considered state-of-the-art. Recently, the development of artificial intelligence (AI) has increased the interest in developing models based on AI. Previous works have shown high potential of AI-aided channel estimation and prediction, which inclines the state-of-the-art title from model-based methods to be confiscated. However, there are many aspects to consider in channel estimation and prediction employed by AI in terms of prediction quality, training complexity, and practical feasibility. To investigate these aspects, this chapter provides an overview of state-of-the-art neural networks, applicable to channel estimation and prediction. The principal neural networks from the overview of channel prediction are empirically compared in terms of prediction quality. An innovative comparative analysis is conducted for five prospective neural netwoion horizons. The widely acknowledged tapped delay line (TDL) channel model, as endorsed by the Third Generation Partnership Project (3GPP), is employed to ensure a standardized evaluation of the neural networks. This comparative assessment enables a comprehensive examination of the merits and demerits inherent in each neural network. Subsequent to this analysis, insights are offered to provide guidelines for the selection of the most appropriate neural network in channel prediction applications.