Today’s competitive world empowers women to lead their own lives, fostering innovation and inevitable growth in the industry. However, inclusivity in the corporate world is still a challenge for women. They have the pressure to meet societal expectations, familial responsibilities, and physiological changes associated with different stages of life. A concerning trend in society is the exponential increase in stress levels among women, a reflection of the evolving challenges and demands that women encounter to make themselves stand out from the rest. Various researchers have found that the number of women experiencing work-related stress is 50% higher than the number of men of the same age. The growing use of wearable Internet of Things (IoT) devices provides an opportunity to expand stress monitoring and intervention techniques in various situations, especially those that might be life-threatening. This study sought to confidently gain insights on stress monitoring for women through data collected by wearable IoT devices by segregating data based on device types. Applying a classification algorithm (transformer model) to determine the accuracy of stress indicators for these devices led us to build the stress accuracy prediction model. The existing BERT model is enhanced to process data beyond plain text. It is designed to uncover hidden patterns and trends associated with women’s stress levels based on their pulse rates. These comparisons are portrayed using data visualizations. Using this enhanced BERT model adapted from the existing numerical algorithm, categorical data from IoT wearable devices is tested to accurately predict stress levels among women. This analysis demonstrates high predictive accuracy, with the earring IoT device achieving the highest accuracy of approximately 92%, indicating the effectiveness of the proposed model in stress monitoring across different wearable devices (earring, ring, and shoe).