This chapter discusses the interaction between human emotional experiences and machine learning frameworks, relating computational methods of emotion generation and recognition to psychological concepts like Ekman’s basic emotions and dimensional theories. We discuss how such models as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers perceive the facial, vocal, and textual features to identify emotions, and also synthesize artificial expressions. Notably, the ethical considerations of privacy, algorithmic bias, manipulation, and accountability are the key concerns that the chapter considers as the core of responsible AI development. The examples of emotion AI in practice (e.g., mental health screening, customer service chatbots, retail personalization) demonstrate how it can transform the world, whereas the questions about the emotional understanding and the effects on society are open-ended, which preconditions the future research. Overall, the chapter suggests that the success of emotion AI lies in the ability to balance the innovativeness with the ethical rigor, so that the technology should improve, but not corrupt, human connection.