Combining deep learning algorithms, mental health assessments, and sarcasm recognition creates an intriguing synergy for deciphering emotional states in textual data. Utilizing cutting-edge technologies to predict mental health presents prospects for early identification and intervention. The primary factors impacted by people's snarky remarks or sarcastic statements, unfavorable tweets, and stress at work. It either directly or indirectly contributes to a person's descent into depression. In India, individuals find it difficult to acknowledge that they are depressed or experiencing mental health issues. They also find it difficult to discuss or disclose their problems with others or to talk about their mental health. It is imperative to identify a person's depression early on. New approaches must be used to diagnose and track symptoms of depression on an everyday basis. In this work, we have employed a novel approach to depression prediction, utilizing sophisticated machine learning algorithms and deep data analysis methods. As compared to the machine learning (ML) algorithm Deep learning algorithms provide better accuracy. The suggested neural network approach extends the capabilities of a pre-trained transformer-based architecture through the strategic integration of convolutional neural network (CNN) and long short-term memory (LSTM) components. In this, we have used Natural Language Processing (NLP) techniques to analyze large amounts of sarcastic utterances in order to identify the sarcastic terms that have a detrimental impact on people's mental health.