Emotion prediction from images is achieved by enabling computers to recognize human emotions based solely on facial appearances. Images or audio recordings of individuals are used to infer whether emotions such as happiness, sadness, anger, surprise, fear, or others are being displayed. In the case of images, facial features including the eyes, mouth, and eyebrows are analyzed, and patterns of change associated with different emotional expressions are learned. These features are carefully examined to identify subtle differences that correspond to specific emotions. Regarding audio, essential characteristics such as pitch, tone, loudness, and speech rate are extracted to assist in the emotion prediction process. Various machine learning and deep learning models are then trained on labeled datasets to enable accurate predictions. Through these approaches, emotion recognition systems are developed to support applications in areas such as healthcare, education, entertainment, and human–computer interaction.