This comprehensive paper delves deep into the realm of multimodal emotion recognition, a fascinating interdisciplinary domain at the intersection of psychology, computer science, and artificial intelligence. The focus is on presenting an all-encompassing exploration of this field, defining key concepts, highlighting potential applications, and delving into the core technologies underpinning it. The paper begins with the definition of multimodal emotion recognition, unraveling its intricacies and importance in various fields. It provides a lucid explanation of how this technology enables machines to understand, interpret, and respond to human emotions by leveraging different modalities such as facial expressions, vocal intonations, body language, and even physiological signals. The significant role of feature extraction techniques in emotion recognition is discussed extensively, shedding light on how these methods extract crucial emotion-related information from a sea of raw data. This process is central to the overall efficiency and accuracy of emotion recognition systems. Additionally, modal fusion, another pivotal element, is explored in-depth. This procedure is integral to the process of combining data from different modalities to form a cohesive and comprehensive representation of emotions, thereby improving the accuracy of emotion recognition.