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Stress in humans is associated with a variety of mental and physical health outcomes as well as functioning and performance in the work setting. Measuring stress in a continuous and non-intrusive way by using wearable technology and smartphones, can potentially serve as an early warning system for long- and short-term effects on health and performance. The increasing ubiquity of mobile phones has made it easier than ever to collect voice data in naturalistic, real-world settings. While most prior research on stress detection from voice has been conducted in lab settings, this study takes a novel approach by analyzing voice recordings collected in real-life conditions. Specifically, we conduct a secondary analysis of data from a previous study that tested the usability and feasibility of collecting daily stress measurements via semi-automated WhatsApp conversations. That study demonstrated the practicality of using mobile messaging to gather self-reported stress data and voice messages at the end of participants’ workdays over the course of two weeks. Similarly, recent advances in computational power and ML techniques have opened new possibilities for analyzing more complex data in mental health research. By leveraging these tools, we aim to evaluate the predictive performance of different modeling approaches and assess the potential of voice-based stress detection in real-world settings—contributing to the development of scalable, passive mental health monitoring systems. Therefore, the current study has two main objectives: 1) To examine whether there is a relationship between voice features recorded under natural circumstances and self-reported stress levels in a population of working adults and 2) to compare the performance of traditional regression models and machine learning (ML) approaches in predicting stress from voice data.