Emotion recognition has the potential to significantly improve human–computer interaction and healthcare applications using biomedical signals. Understanding human behavior and mental health relies heavily on emotion recognition. This chapter gives a succinct description and implementation of artificial intelligence (AI) algorithms for emotion recognition using electrocardiogram (ECG) signals. ECG signals, which represent the electrical activity of the heart, have been investigated as a potential physiological biomarker of emotional states. AI techniques, such as machine learning and deep learning algorithms, have been used to analyze ECG data and properly classify emotions. These methods collect features from ECG signals and train models to recognize patterns associated with various emotional states. The use of AI for emotion recognition using ECG has yielded promising results in a variety of domains, including mental health evaluation, affective computing, and human–computer interaction. ECG signals and signal processing methods have been used to automate the detection and classification of emotions. The purpose of this chapter is to demonstrate how to create an efficient Python ecosystem for real-time emotion recognition from ECG signals using PYTHON. The system's performance was assessed on a variety of individuals exposed to controlled emotional stimuli. The results confirmed the system's efficacy in accurately discriminating emotions, highlighting the promise of ECG-based emotion detection. This study advances emotion identification systems, opening the door to new applications in human–computer interaction, and mental health monitoring.