Abstract: Stress is a major factor causing many diseases in today society. In this survey, the paper explore recent research that focuses on detecting stress in everyday life using various techniques. While there are studies on stress detection in controlled lab settings, there is a shortage of research specifically looking at how stress is identified in real-life, day- to-day circumstances. A thorough review of machine learning-based stress detection is given in this research study. Various algorithms, ranging from traditional statistical methods to advanced deep learning models, are scrutinized for their effectiveness. By examining detection techniques, conducting a thorough literature survey, and evaluating the advantages and disadvantages of machine learning algorithms, the paper contributes to the evolving landscape of technology-assisted mental health interventions. The findings aim to guide future research endeavors, fostering responsible and impactful applications of machine learning in the domain of stress detection and mitigation.