Emotion recognition from electroencephalography (EEG) has become a revolutionary field, with tremendous potential in applications like mental health, human–computer interaction, and adaptive learning systems. This paper provides a comprehensive review of the latest developments in EEG-based emotion recognition, including novel methodologies, cutting-edge algorithms, and upcoming trends in the field. A thorough literature review is carried out, emphasizing the techniques of feature extraction, classification models, and the combination of deep learning paradigms to achieve higher accuracy and reliability. This research also discusses the difficulties in EEG signal processing, such as noise filtering, inter-subject differences, and limited datasets. The impact of hybrid models and multimodal strategies in extending emotion detection functionalities is also deliberated. By integrating the latest research studies, this review seeks to shed important light on the future path of EEG-based emotion recognition and propose possible avenues for future exploration and development. The results emphasize the revolutionary effects of sophisticated EEG-based systems in understanding and interpreting human emotions in real time.