As technology in data processing and machine learning has advanced, the research approaches of emotion recognition, affective computing, and sentiment analysis have become increasingly interconnected. The emergence of techniques such as deep learning has revolutionized the way we address these subjects, enabling more precise and sophisticated analysis of emotional expressions in multimodal data, including images, audio, text, and physiological signals [13; 17; 18; 19]. Furthermore, these disciplines have naturally begun to merge as real-world applications, such as healthcare [4; 20]; human-machine interaction like in the analysis of drivers' emotions [21; 22]; e-learning [23]; and social media data analysis [24; 25]. These applications demand integrated approaches. In the literature, there are noteworthy recent reviews covering topics such as emotion recognition, affective computing, and sentiment analysis. Several of these reviews exhibit diverse approaches and objectives. Some studies concentrate on specific data types, such as those exclusively delving into physiological signal analysis [26; 27; 28] or physical cues, like facial emotion recognition [9; 29]. Some studies focus on the sensors employed for data collection [30; 31], while others examine the types of machine learning algorithms used for data analysis. For example, the study by [PERSON] et al., in 2021, which emphasizes deep learning investigations related to human affect recognition analysis, provides valuable insights and developments [32]. Similarly, the study by [PERSON] et al., published in 2023, systematically reviews studies on multimodal emotion recognition employing machine learning algorithms [33]. However, there is currently no comprehensive review that employs these three specific concepts in its search string terms and strives to collectively address recent studies. Such an approach, involving their categorization based on the type of data they analyze, would facilitate a comprehensive understanding of how these recent studies, particularly those spanning 2018 to 2023, are interrelated and evolving in terms of trends in modalities, applications, data analysis models, and the origins of their analytic data. This analysis is crucial because it allows for the identification of best practices across different research themes. By comparing studies that may initially appear unrelated but utilize similar modalities in their data, we can uncover valuable insights and techniques that can be applied across various other fields, such as cognitive sciences. This multidisciplinary perspective not only enhances our understanding of the advancements in each field, but also promotes the adoption of successful practices from one area to another, enriching the overall research landscape. This study aims to provide a comprehensive overview of how these fields have advanced in terms of applications, learning models, and datasets, and how they have established essential connections to address the complex demands of everyday life in an increasingly digital and user-experience-oriented world. The rest of this document is organized as follows. Section 2 describes the methodology employed, including the research questions and the search process to select relevant studies. Section 3 presents the results. Section 4 provides a comprehensive discussion, delving into the major findings in detail. Lastly, Section 5 is dedicated to the conclusions drawn from the analysis.