Depression is a serious mental health condition that generallysee a unnoticed or untreated because there is not to much awareness about it. Most people don’t really understand what it is, and it’s not always diagnosed using proper or traditional methods.Traditional approaches, such as personal evaluations and doctor consultations, is typically subjective and influenced by external factors like mood, surroundings, or social pressure. This survey attempts to explore a crossplatform system for detecting depression by analyzing text, audio, and images. By conducting systematic and well-structured survey, we hope to understand public perceptions of AI-based mental health monitoring anddetermine how ready theyare to accept such te chnology.The survey highlights the importance like the accuracy of text sentiment analysis, speech emotion recognition, and facial expression detectionthrough images to identify patterns related to depression. It also looks user about regarding privacy, trust, and the ethical use of AIbased detection systems. By gathering insights from various participants, this study offers a clear understanding of public attitudes toward multimodal depression detection method. The findings of this survey may provide to future advancements in AI-based mental health monitoring, leading to a more precise and consistent understanding of emotional well-being.