Human stress level detection is an important technology that is the need of the hour. This research presents a novel approach to determine human stress levels by analyzing physiological data using a hybrid machine learning model that combines AdaBoost and Random Forest classifiers. This study recognizes the limitations of subjective stress assessments and instead employs objective physiological markers, such as heart rate variability, galvanic skin response, and body temperature, as reliable predictors of stress. The aim of the proposed hybrid model is to utilize the benefits of AdaBoost's capacity to enhance weak learners and Random Forest's capability to handle overfitting and handle data with high dimensions. The methodology consists of meticulous data preprocessing, model training, and validation stages, ensuring the robustness and dependability of the proposed strategy. The experimental findings demonstrate the model's performance, reaching near-perfect accuracy, precision, recall, and F1-score metrics. This study significantly advances the field of stress detection by presenting an intelligent and effective approach that has the capacity to dramatically improve health monitoring and personalized stress management strategies.