Stress is the reality of ordinary day-to-day existence. Everyone experiences stress at some point of time in life. A limited amount of stress can help you increase your physical performance, motivation, and environmental reaction time, but stress can become a severe problem if left unchecked. Studies have shown that stress increases the likelihood of developing health problems like heart disease, anxiety disorders, depression, gastrointestinal issues, etc., and endangers human life. More and more people are stressed due to the fast-paced nature of modern society, and some are even willing to commit suicide. With the adverse effects of stress in mind and a lack of resources for early detection, the proposed approach is to create a model for stress detection using social media posts. The reason for using social media is that people post about their recent happenings on social media platforms, which can be used to represent their current and contemporary situation. Stress is inferred through a two-step process: personality traits are first estimated from a user’s social media activity, after which these traits are integrated with linguistic cues to assess stress levels. Evaluation conducted on two widely adopted datasets demonstrates that the proposed method maintains strong reliability and outperforms comparable techniques. By fusing linguistic cues with personality-related attributes, the framework achieves notably improved stress identification across heterogeneous users and varying data conditions.