Stress substantially impacts both intellectual and physical health, growing the likelihood of mental troubles and chronic infection. This study indicates a completely unique combinational method for strain detection that combines mental and physical facts from wearable sensors (watches). In order to improve the accuracy of strain identity, emotional analysis is carried out to voice-transformed textual content information in the suggested observe. The method analyses textual content information and extracts the sentiment or emotional tone of a sentence the use of the Convolutional Neural Network (CNN) model for Natural Language Processing (NLP). To get more specific and beneficial findings, the bodily traits of the user are integrated with the sentimental analysis output generated by using a gadget learning model. After then, this information is stored on document for a totally long time. In order to higher classify the person's pressure stage, this statistic is then collected over an extended time period and shown as graphs. The aim of the undertaking is to increase a novel and green method of stress detection as a way to allow human beings to control their strain ranges.