Emotions significantly influence human behaviour, decision-making, and communication, making their accurate recognition essential for various applications. This study introduces a novel approach for emotion extraction from electrocardiogram (ECG) and galvanic skin response (GSR) signals using Bidirectional Long Short-Term Memory (BiLSTM) networks. Unlike conventional emotion recognition methods that rely on facial expressions or self-reports, our model utilizes physiological signals to capture emotional states with high precision. ECG provides insights into cardiac activity, while GSR reflects changes in skin conductance, both serving as reliable indicators of emotional responses. By leveraging advanced signal processing techniques and deep learning algorithms, the model effectively identifies intricate patterns within these biosignals, enabling accurate emotion classification. Experimental validation demonstrates the model’s effectiveness in distinguishing between different emotional states, surpassing traditional methods. This research contributes to affective computing and human-computer interaction (HCI) by enhancing the capability of intelligent systems to recognize and respond to human emotions, paving the way for applications in mental health monitoring, driver assistance systems, and adaptive user interfaces.