This paper discusses the real-time application development for Postpartum Depression detection based on facial expression, questionnaire-based identification, and alarm system. Research shows that first-time mothers with PPD experience dramatic changes in their emotional foment as objectively measured with modern facial recognition technology. The application will monitor the mother by taking real-time facial expressions to show how is she feeling whether happy, surprise, neutral, anger, sadness, disgust, or fear. Thus, the evaluation will be done for mother and only the emotion value will be saved for further process. After saving the feelings, the mother will also be asked to fill in the weekly and daily questionnaires based on Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9. By doing this, the mother gets immediate feedback on her emotional states, which reinforces the possibility of timely intervention. Moreover, the output will be calculated with Decision Tree for emotion recognition and questionnaire data, and the alarm trigger will be activated if the criteria are met. It gives a heads up to support systems if a negative emotional threshold is crossed. The presented framework incorporates novel data sources to detect PPD and deliver pre-emptive mental health support, resulting in improved health outcomes for both mothers and their babies.