Accurate stress detection is vital in high-stakes industries where the smallest error can lead to significant consequences. Human error, often driven by unmanaged stress, remains a persistent challenge. This has intensified research into the administration of mental capability, consideration, and pressure. Identifying when individuals are under excessive stress when they become overwhelmed or their cognitive function deteriorates- is crucial for preventing serious incidents. Understanding neurophysiological and cognitive behavioral responses to stress can also guide the optimization of equipment, procedures, and work environments, thereby reducing risks and enhancing safety. Effective stress management is not only essential for maintaining safety and operational efficiency but also for improving the overall physical and mental well-being of humans. In this context, our paper explores current research and methodologies to develop strategies and interventions that alleviate the negative effects of stress. Additionally, we introduce a stress detection model using a deep learning algorithm, specifically a Convolutional Neural Network (CNN), to identify stress through facial image analysis. By accurately detecting stress, this method focuses to improve safety, optimize performance, and promote well-being in high-pressure environments.