This chapter summarizes the comprehensive presentation of multimodal learning techniques in recommendation systems which focused on five critical challenges: noise and redundant features, user diverse preference, user modality preference, dynamic multimodal fusion, and the interpretability and controllability of recommendations. Despite the extensive discussion and proposed solutions for the key technical challenges, we recognize that this research area is still in its early stages, with considerable opportunities for further exploration. In this regard, the book outlines several promising directions and challenges, aiming to inspire and guide future research in this evolving domain.