Depression is a pervasive medical condition that profoundly impacts individuals’ emotions, thoughts, and behaviors, often leading to comorbidities like anxiety, bipolar disorder, sleep disturbances, and in severe cases, self-harm or suicide. Traditional depression detection methods, like patient interviews and standardized questionnaires, have accuracy limitations. This research aimed to develop a mental health status indicator tool by centering on emotional stability assessment scores for individuals and pre-screening tests for professionals on the mental health status of the individuals. It aims to design a novel emotional stability assessment form using both constructive and diagnostic aspects of the structured questionnaire in line with DASS 21. It helps to address the hesitation of individuals in seeking professional help by knowing the self-assessment score and understanding the severity level. A study has been conducted based on a proposed emotional stability assessment survey on age groups between18 to 22. A depression prediction model was built using a Machine Learning approach using a Support Vector Machine (SVM), Gradient Boosting (GB), Random Forest (RF), and Stacking. The proposed stacking model ensures 91% accuracy among other methods. This research contributes significantly to enhancing depression detection methodologies in the early stage, particularly among young adults, and facilitates timely intervention and support.