Scientists and researchers have tried to establish a bond between the emotions conveyed and the subsequent mood perceived in a person. Emotions play a major role in terms of our choices, preferences, and decision-making. Emotions appear whenever a person perceives a change in their surroundings or within their body. Since early times, a considerable amount of effort has been made in the field of emotion detection and mood estimation. Listening to music forms a major part of our daily life. The music we listen to, the emotions it induces, and the resulting mood are all interrelated in ways we are unbeknownst to, and our survey is entirely based on these two areas of research. Differing viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This paper provides a detailed review of the methods proposed in music mood recognition. It also discusses the different sensors that have been utilized to acquire various physiological signals. This paper will focus upon the datasets created and reused, different classifiers employed to obtain results with higher accuracy, features extracted from the acquired signals, and music along with an attempt to determine the exact features and parameters that will help in improving the classification process. It will also investigate several techniques to detect emotions and the different music models used to assess the music mood. This review intends to answer the questions and research issues in identifying human emotions and music mood to provide a greater insight into this field of interest and develop a better understanding to comprehend and answer the perplexing problems that surround us.