The ability to accurately recognize another person's emotional state is a fundamental skill for psychotherapists, as it enables empathy and effective therapeutic interventions.[1] This study explores the application of facial emotion recognition (FER) technology in the context of therapy and training sessions. FER systems, utilizing advanced computer vision and machine learning techniques, can analyze facial expressions to identify and classify emotions such as happiness, sadness, anger, fear, surprise, and disgust.[1] By integrating FER into therapeutic settings, clinicians can gain objective insights into patients' emotional responses, leading to more tailored and effective treatments.[1] Furthermore, FER can assist in research by providing reliable and quantitative measures of psychotherapy process ratings, enhancing the validity and generalizability of findings. This study aims to demonstrate the potential benefits of FER in improving the quality and effectiveness of psychotherapy and training, ultimately contributing to advancements in mental health care.