The field of emotion recognition is rapidly growing within artificial intelligence (AI), employing machine learning (ML) and deep learning (DL) to interpret human emotions. This chapter offers an overview of the domain, beginning with fundamental concepts and examining the significance and challenges involved. It explores the latest techniques, such as Support Vector Machines (SVMs), Long Short-Term Memory networks (LSTMs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs). A key focus is on integrating these models with multimodal data including speech, text, facial expressions, and physiological signals to improve the accuracy of emotion recognition systems. Literature research was conducted using the “PRISMA 2020“guidelines. The chapter also addresses practical applications, particularly in healthcare, education, and customer service, where emotion recognition can bring transformative change. It further delves into privacy concerns and ethical issues, highlighting the importance of developing systems that respect user privacy and ethical values. Challenges such as algorithmic bias and data privacy concerns are critically examined, alongside the need for transparent and responsible AI systems. Moreover, the chapter underscores the importance of establishing ethical standards and legal frameworks to govern the use of emotion recognition technologies. It discusses the potential risks these technologies pose and advocates for the creation of systems that are interpretable, culturally sensitive, and aligned with the greater good of society. In conclusion, a forward-looking perspective is provided, outlining future research directions, calling for interdisciplinary collaboration, and stressing the development of models that ensure ethical and socially responsible emotion recognition systems.