This research presents a novel Cloud-Assisted IoT Framework for Human Stress Rate Monitoring using a Bayesian Network. Stress has become a prevalent issue in modern society, affecting individuals' well-being and performance. To address this, a comprehensive framework is developed that combines various sensors to measure and monitor stress levels. The sensor array includes a camera as a visual sensor, an EEG sensor for psychological monitoring, accelerometers for capturing behavioral data, and an ECG sensor for assessing heart rate as a performance indicator. Real-time data is collected from these sensors while participants engage in computer-based work. The collected sensor data is then processed and integrated into a Dynamic Bayesian Network (DBN) to calculate the stress rate accurately. The DBN construction involves modeling the dependencies and relationships among the sensor inputs, allowing for the estimation of conditional probabilities, transition probabilities, and likelihoods of observed data. The results show significant correlations between the sensor data and stress rates, with parameters such as eye blink frequency, EEG values, accelerometer readings, and ECG values exhibiting consistent increases with higher stress levels. The developed framework and DBN-based approach offer a comprehensive and effective solution for monitoring and managing human stress levels. The integration of multiple sensors and the utilization of Bayesian Network inference provide real-time stress monitoring and personalized interventions. This research contributes to the field of stress monitoring and management, with implications for healthcare, workplace well-being, and performance optimization. By accurately measuring and addressing stress levels, individuals can improve their overall quality of life and achieve better outcomes in various aspects of their daily activities.