This research explores the potential of Internet of Things (IoT)-enabled wearable sensors in conjunction with machine learning techniques for real-time, non-invasive stress monitoring in women, using physiological data indicative of stress states. An “IoT Wearables Dataset for Women’s Safety: Stress Detection and Analysis” was sourced from IEEE Data port and was used to train four supervised machine learning models, namely, random forest, support vector machines (SVM), gradient boosting, and logistic regression to classify individuals into categories of “stressed” and “unstressed” based on a predefined threshold of 0.35. The random forest algorithm attained the highest accuracy of 88.5% in categorizing stress, demonstrating reliable capabilities in identifying stress indicators from wearable sensor data.