Suicide is a significant global mental and public health issue, causing 800,000 deaths per year. The Asian continent accounts for more than half of the ensuing 800,000 deaths. Suicide is the second-leading cause of death in the age group between 15 and 29 years. Anxiety and depression are two common mental health conditions that are prevalent in adolescents and young adults. When left untreated, they potentially lead to self-harm and suicide. Intelligent screening systems designed by using machine learning models can provide crucial aid for timely diagnosis and prevention of self-harm. . The efficiency of an intelligent screening model is contingent on identifying the correct markers that are relevant and essential for identifying an underlying condition. A survey of Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7). Three hundred and forty-six responses were recorded from students at undergraduate and postgraduate levels from south India. In order to detect the potential candidates who are at short term risk for self-harm, the study put together few machine learning algorithms that are appropriate for predictive modelling problems. The machine learning models built on the algorithms K-Nearest neighbour (KNN), Support vector regression (SVR), Decision trees (DT), and Random Forest (RF) performed with accuracy scores of 0.74, 0.65, 0.739, 0.971 respectively

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