Stress can be termed as any kind of physical, emotional, and psychological imbalances in the human body. Stress is the way one reacts to anything that requires attention. Living beings can withstand stress to some extent. The way people react to stress indeed makes a large difference to their complete well-being. With various alterations in one’s personal and professional life, there is an advancement in the threat of stress. However, many public and private sectors incorporate many stress relief measures and make an effort to create ease at the place of work. With the help of this chapter, we put forward a type of machine learning (ML) and natural language processing (NLP) methodology that detects stress on a single entity with the help of social media posts’ dataset recorded from individual posts and information posted by a user. These posts are collected as datasets and preprocessed, which are used for prediction and alerting the user; in this way, we can protect a person from a wide range of stress-related mental health problems. Various social media posts from Reddit that contain text and quotes are gathered to a dataset. These raw datasets can be helpful in analyzing physiological conditions and are taken from Kaggle dataset. The machine learning model is created that classifies the data and predicts whether the person is stressed or not when a new post is posted. Further, the status of the person is intimated through a Telegram message that is sent automatically using a chatbot. NLP is used to create a word cloud that collects important stressful phrases and makes classification easier. Machine learning techniques like Naïve Bayes classification, Bernoulli classification, and many Python packages are utilized. During the study, by using machine learning techniques and NLP, the accuracy of the project increased from 81.25% to 85%. Along with classification, the messages are delivered without delay with an accuracy of 90%.