Imaging technology has evolved significantly, offering unprecedented insights across diverse domains. This research explores the multifaceted applications and implications of infrared thermography (IRTG) in human healthcare monitoring. IRTG has emerged as a valuable adjunctive tool in healthcare, offering radiation-free and non-invasive insights into physiological processes and psychological conditions through the visualization of surface temperature variations. In clinical diagnostics, thermal imaging enables early detection and monitoring of various conditions, including musculoskeletal injuries, inflammatory disorders, and psychosomatic abnormalities. By capturing thermal patterns associated with tissue perfusion and metabolic activity, thermal imaging aids in differential diagnosis and treatment planning, particularly in specialities such as rheumatology, sports medicine, and human emotions. Fatigue may involve a lack of energy and feelings of exhaustion, as well as difficulty in performing voluntary tasks. Muscle fatigue may lead to muscle injury and a drop in alertness when not diagnosed and treated in the early stages. Early and timely detection of fatigue is hence essential for developing and using suitable countermeasures. In this work, we are proposing and validating a method to record the database for exercise-induced fatigue detection. As there is no database to detect exercise-induced fatigue, the key area of focus in this study is to generate a novel dataset to identify the region of interest of facial thermal images for the detection of physical fatigue and emotion recognition. This work combines advanced Computer vision and thermal imaging technology that allows the detection of fatigue in the human body and the possibility of making real-time human emotion recognition systems more affordable and portable. In this work, we also propose a model for predicting human emotions from facial thermal images. With an average detection accuracy of 0.9857 and improved detection efficiency, the model showcases its effectiveness in emotion prediction. The aim of this research is to see if the results from the experiment can support the case for using infrared thermography as a human monitoring method, primarily to identify fatigue levels with human emotions.