In high-stress professions like nursing, continuous monitoring of physiological indicators such as heart rate, electrodermal activity, and skin temperature offers a promising approach for predicting and managing stress through wearable IoT devices. This study employs machine learning algorithms to analyze real-time physiological data collected during hospital shifts and measure the stress conditions of nurses. Moreover, the predictions generated by these models can significantly enhance stress management among nurses by allowing things like workload adjustments or personalized relaxation techniques. Over the long term, such data can aid in the formulation of policies on how hospital shifts are organized and how occupational stress is managed. Stress mitigation strategies are IoT-based models that reduce negative work effects on nurses and optimize patient outcomes. This study underscores the potential of wearable technology combined with machine learning for stress detection, benefiting both individual healthcare professionals and the broader healthcare system by promoting mental and physical resilience among nurses for optimal patient care.