  This two-part paper investigates the application of artificial intelligence
(AI) and in particular machine learning (ML) to the study of wireless
propagation channels. In Part I, we introduced AI and ML as well as provided a
comprehensive survey on ML enabled channel characterization and antenna-channel
optimization, and in this part (Part II) we review state-of-the-art literature
on scenario identification and channel modeling here. In particular, the key
ideas of ML for scenario identification and channel modeling/prediction are
presented, and the widely used ML methods for propagation scenario
identification and channel modeling and prediction are analyzed and compared.
Based on the state-of-art, the future challenges of AI/ML-based channel data
processing techniques are given as well.
