This study presents the improvement and execution of a facial feeling acknowledgment framework planned to identify mental stretch in understudies through a kiosk-based approach utilizing Raspberry Pi. The essential objective of this framework is to upgrade the real-time checking and administration of mental wellbeing by coordination of different electronic components and machine learning models to distinguish passionate states characteristic of stretch or mental strain. The stand framework utilizes an RPI 5 microcontroller due to its high-speed information transmission capabilities and broad meddle alternatives, basic for interfacing numerous sensors and modules. The MAX30100 sensor screens heart rate through photoplethysmography, whereas the MPU6050 sensor tracks physical action and rest designs. The INMP441 receiver captures discourse, which is vital for surveying passionate states through sound examination. Each sensor is carefully chosen for its usefulness and compatibility with the RPI 5, guaranteeing solid information collection and preparing. The kiosk's plan highlights a user-friendly interface with an OLED show for real-time criticism, and information administration is encouraged through an SD card module. The technique consolidates few essential machine learning approaches for push location: a Irregular Timberland Classifier for heart rate information and a Convolutional Neural Arrange (CNN) for discourse investigation. The CNN show forms sound recordings changed over into spectrograms to classify passionate states based on discourse designs. The model's preparation included a comprehensive dataset of discourse tests from different clutter stages, with execution measurements demonstrating tall precision and unwavering quality. Information transmission between the stand and the computer framework is accomplished by means of Wi-Fi, leveraging the RPI 5's capabilities. The collected information is prepared by machine learning models running on the computer, with forecasts shown on the kiosk's OLED screen and a client interface on the computer. The application consolidates cognitive behavioral treatment (CBT) standards, counting stage discovery, trigger recognizable proof, and Socratic addressing, to back clients in overseeing their mental wellbeing.