This paper provides a comprehensive review of key technologies for emotion analysis using multimodal information. Emotion analysis, an essential part of human-machine interactions, has seen significant progress in recent years. With the rise of multimodal data sources such as speech, text, and images, there is a growing need for a thorough review of important elements in this field. The paper explores multimodal data sources including speech, text, images, and physiological signals. It provides an overview of relevant literature, academic forums, and competitions. The paper emphasizes the analysis of unimodal processing methods, including preprocessing, feature extraction, and tools across speech, text, images, and physiological signals. It also discusses the nuances of multimodal data fusion techniques, highlighting early, late, model, and hybrid fusion strategies. Key findings indicate the importance of analyzing emotions across multiple modalities. The paper presents detailed discussions on emotion elicitation, expression, and representation models. It also uncovers challenges such as dataset creation, modality synchronization, model efficiency, limited data scenarios, cross-domain applicability, and the handling of missing modalities. Practical solutions and suggestions are provided to address these challenges. The realm of multimodal emotion analysis is vast, with numerous applications ranging from driver sentiment detection to medical evaluations. The paper serves as a valuable resource for both scholars and industry professionals. It not only sheds light on the current state of research but also highlights potential directions for future innovations. The insights from this paper are expected to pave the way for subsequent advancements in deep multimodal emotion analysis tailored for real-world deployments. Keywords: Multimodal information, Emotional analysis, Multimodal fusion.This paper provides a comprehensive review of key technologies for emotion analysis using multimodal information. Emotion analysis, an essential part of human-machine interactions, has seen significant progress in recent years. With the rise of multimodal data sources such as speech, text, and images, there is a growing need for a thorough review of important elements in this field. The paper explores multimodal data sources including speech, text, images, and physiological signals. It provides an overview of relevant literature, academic forums, and competitions. The paper emphasizes the analysis of unimodal processing methods, including preprocessing, feature extraction, and tools across speech, text, images, and physiological signals. It also discusses the nuances of multimodal data fusion techniques, highlighting early, late, model, and hybrid fusion strategies. Key findings indicate the importance of analyzing emotions across multiple modalities. The paper presents detailed discussions on emotion elicitation, expression, and representation models. It also uncovers challenges such as dataset creation, modality synchronization, model efficiency, limited data scenarios, cross-domain applicability, and the handling of missing modalities. Practical solutions and suggestions are provided to address these challenges. The realm of multimodal emotion analysis is vast, with numerous applications ranging from driver sentiment detection to medical evaluations. The paper serves as a valuable resource for both scholars and industry professionals. It not only sheds light on the current state of research but also highlights potential directions for future innovations. The insights from this paper are expected to pave the way for subsequent advancements in deep multimodal emotion analysis tailored for real-world deployments. Keywords: Multimodal information, Emotional analysis, Multimodal fusion.