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Many people in today's society experience anxiety, depression, and heart disease as direct results of stress. An increasing number of people and medical professionals recognizethe need for efficient stress monitoring and management toolsto track and alleviate stress in real time. In order to fill this gap, the proposed stress monitoring framework makes use of vocal and facial expressions asindicators of stress. Frowziness, furrowed brows, and narrowed eyes are all telltale signs of stress, as changes in vocal pitch, volume, and rate. The proposed system uses signal processing techniques to extract stress-related features from these expressions and classify them as indicative of stress or not. Stress-related characteristics can be accurately classifiedusing machine learning models like neural networks, SVM, and Random Forest in this setting. The proposed system enhancesthe accuracy and robustness of the stress monitoring tool bycombining the results of multiple decision trees trained usingRandom Forest on different subsets of data and features. An intuitive interface that shows current stress levels has been created to make the system more approachable. The mental health field, the medical field, and related fields can all benefit from this interface. For instance, it can be used by mental health professionals to better diagnose and track their patients' stress levels over time, allowing for more precise and timely interventions. In addition, it can teach people how to controltheir own stress and show them how their thoughts, feelings,and actions all contribute to their health. Finally, the proposed stress monitoring framework providesa robust and effective method for tracking stress levels in real-time. The system's robustness and accuracy are due to the integration of signal processing techniques and machine learning algorithms, which can be applied in a number of fields, including healthcare and personal wellness. Designed with simplicity in mind, the system is a great resource for copingwith the stresses of modern life. [ABSTRACT FROM AUTHOR]