Emotion recognition is a critical aspect of human communication, enabling us to understand and respond to others’ feelings. Accurately recognizing emotions through technology holds immense potential for various applications, particularly in human–computer interaction (HCI). Our method effectively tracts the features from the audio, image, and hybrid signals. Our exploration consists of three phases: Speech Emotion Recognition (SER), Facial Emotion Recognition (FER), and Multimodal Emotion Recognition (MER). In this paper, we utilize features such as Mel-frequency cepstral coefficients (MFCC), Mel spectrogram, Tonnetz, and Chroma. FER utilizes 2D Convolutional Neural Network (CNN) architectures, namely ResNet50 and DenseNet121, to achieve its objectives. When MER integrates both audio and visual pathways, it yields encouraging outcomes. For SER, we utilized different machine learning and deep learning models on the features extracted by our method. Among these models, the 1D CNN model demonstrates superior performance compared to the others. This research focuses on studying MER using publicly available datasets, namely a FER dataset and an audio-only and audio–video dataset called RAVDESS. The dataset includes various emotions expressed at different intensity levels, including calm, happy, sad, angry, fearful, surprise, and disgust. The results of our study illustrate the effectiveness of MER in identifying these emotions and offer valuable insights for enhancing human–computer interaction.