The project aims to develop a system for mental health prediction using facial image and sentiment analysis. The system will use computer vision and natural language processing techniques to analyze facial expressions and sentiments expressed in text to predict the likelihood of a person experiencing mental health issues such as anxiety, depression, or stress. The proposed system will leverage deep learning models to extract features from facial images, such as facial expressions and emotions, and combine them with sentiment analysis results derived from text input. The system will then use these features to predict the likelihood of the person experiencing mental health issues. To develop the system, a dataset of facial images and text inputs will be collected from individuals with and without mental health issues. The dataset will be annotated by mental health professionals to indicate the presence or absence of mental health issues in each sample. The system's performance will be evaluated using various metrics, such as accuracy, sensitivity, and specificity, to determine its effectiveness in predicting mental health issues accurately. If successful this system could help healthcare professionals and organizations to identify individuals at risk of mental health issues early allowing for prompt intervention and support.