Using data from the Behavioral Risk Factor Surveillance System (BRFSS) and automated learning methods, this research intends to assess veteran stress. Veteran stress might be attributed in part to the specific difficulties many veterans encounter as a result of their military service. In order to create effective therapies as well as assistance networks, it is essential to get an in-depth knowledge of and approach to stress in this group [1]. The BRFSS dataset, compiled by the CDC, is a precious asset for this investigation because of the breadth and depth of the data it contains on a wide range of habits associated with health and disorders. This study analyzes the BRFSS data in order to learn more about the causes of veterans' stress via a variety of statistical methods, including descriptive statistics, inferential statistics, regression analysis, and classification techniques. Demographic characteristics including age, gender, ethnicity, degree of education, and length of military service are used in statistical analysis to provide a picture of the veteran community[2]. In order to get a better knowledge of the factors that may contribute to stress amongst veterans, inductive statistics are used to discover major relationships between parameters and stress levels. Algorithms that forecast stress levels are developed using the technique of regression analysis. Socioeconomic status, lifestyle decisions, health status, and access to medical care are just few of the factors that go into the models of multiple regression that are created. The study's primary objective is to determine what characteristics of veterans' lives are most associated with elevated stress. In order to further forecast stress levels according to multiple indicators, approaches to classification such as logistic regression, decision trees, random forests, and support vector machines are used [3]. These automated learning methods create algorithms that can predict an individual service member's stress level, which might help pinpoint at-risk service members and guide more precise treatment plans. Suitable parameters are used to measure the algorithms' effectiveness terms of their accuracy and dependability. The results of this research add to our knowledge of the causes of veterans' stress and may help in the design of more effective treatments and services for this population. The well-being and quality of life of veterans may be enhanced by determining effective techniques to treat stress in this group. Finally, the thesis suggests how machine learning approaches may be used to the assessment of veterans' stress.