This paper presents an innovative artificial intelligence (AI) system designed to automatically analyze depression levels by examining visual and vocal expressions. The system leverages advancements in computer vision and natural language processing to extract relevant features from facial expressions and speech patterns. Key methodologies include the use of Convolutional Neural Networks (CNN) for deep learning and feature extraction from facial images and audio segments. The system aims to accurately assess the severity of depression by integrating visual and vocal modalities, addressing the challenges of timely detection and intervention in mental health. The proposed method has shown promising results on the AVEC2014 dataset, demonstrating superior performance compared to hand-crafted features. The paper also reviews existing literature on facial expression analysis, vocal expression analysis, and multimodal fusion techniques for depression detection. The conclusion highlights the effectiveness of the proposed system and its potential to improve mental health assessment and intervention.This paper presents an innovative artificial intelligence (AI) system designed to automatically analyze depression levels by examining visual and vocal expressions. The system leverages advancements in computer vision and natural language processing to extract relevant features from facial expressions and speech patterns. Key methodologies include the use of Convolutional Neural Networks (CNN) for deep learning and feature extraction from facial images and audio segments. The system aims to accurately assess the severity of depression by integrating visual and vocal modalities, addressing the challenges of timely detection and intervention in mental health. The proposed method has shown promising results on the AVEC2014 dataset, demonstrating superior performance compared to hand-crafted features. The paper also reviews existing literature on facial expression analysis, vocal expression analysis, and multimodal fusion techniques for depression detection. The conclusion highlights the effectiveness of the proposed system and its potential to improve mental health assessment and intervention.