Psychological stress poses a significant threat to people’s well-being, and it can be challenging to identify and address stress in a timely manner. With an increase in the usage of social network platforms, people have become accustomed to share their daily routine activities and engage with their friends online. This presents an opportunity to leverage the data from these online social networks for detecting stress proactively. In this research work, real time dataset is collected to investigate the correlation between users’ stress level and their social interactions. A comprehensive set of stress-related attributes, encompassing textual, visual, and social aspects serve as indicators of stress levels in users. A novel model is proposed in this study that can utilize these stress-related attributes to improve detection performance. The model considers the stress states of both the users and their friends in the social network. By analyzing user interactions and shared content, stress levels can be accurately gauged. To make findings of this study accessible to the public, a website is created allowing users to assess their stress levels and explore related activities. By using this platform, individuals can gain insights into their own stress rates and access resources to manage stress effectively. Through the experiments and analysis, the effectiveness of the proposed model in detecting stress is demonstrated. The integration of social network data and stress-related attributes significantly enhances the accuracy of stress detection, enabling proactive care and support for individuals facing psychological stress.