Stress known to be the underlying cause of several mental health disorders, arises from a variety of sources that hurt human health. The consequences of stress are most noticeable in the life of a working professional who must handle the demands of increased management expectations, time management limitations, and family obligations. Physiological traits are crucial for diagnosis of stress-related illnesses and offer valuable insights into the intricate connection between mental and physical well-being. The main motive of this research is to investigate stress using EEG data, which is recognized for its reliability, accuracy, and precision, and to generate a complete system that uses these machine learning models to forecast stress levels that may be classified as Positive, Neutral, or Negative. Sophisticated models for machine learning, like SVM (support vector Machine), KNN, DT (Decision Tree), and Random Forest, are implemented according to intrinsic compatibility between stress signals and EEG data. The paper aims to find best solution for an efficient stress analyzer by merging several methods.