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Emotion recognition is a critical aspect of human-computer interaction and affective computing, with applications ranging from personalized user interfaces to mental health monitoring. This study explores the performance of various machine learning algorithms for emotion recognition using physiological signals obtained from subjects under diverse experimental conditions. The methodology involves conducting a series of carefully designed experiments to collect physiological data from participants. These experiments vary in environmental conditions, including distance from the transmitter and receiver, heart rate variability, and subject posture. Several machine learning algorithms are evaluated in the study and the performance of these algorithms is assessed using various metrics. The results of the experiments demonstrate significant variations in algorithm performance across different experimental conditions. Through comparative analysis, Random Forest emerges as the top-performing algorithm, consistently achieving the lowest MAE values across experiments. However, SVM also demonstrates competitive performance, especially in scenarios involving high heart rates. These findings underscore the importance of algorithm selection and optimization in achieving accurate emotion recognition in real-world applications. In conclusion, this study provides valuable insights into the performance of machine learning algorithms for emotion recognition using physiological signals. By understanding how different factors influence algorithm performance, researchers and practitioners can develop more effective emotion recognition systems for a wide range of applications, including healthcare, human-computer interaction, and affective computing.