Suicide continues to be one of the top causes of death for young adults in India. This research examines suicidal thoughts among college students in Karnataka and analyses how machine learning can help identify individuals at high risk. Utilizing primary data collected from 689 students in five districts and employing the Adult Suicidal Ideation Questionnaire (ASIQ), this study uses decision tree algorithms to identify significant predictors. Results indicate that age, educational path, relationship problems, peer influence, and drug use are key factors. The research suggests a data-centric model to assist educational organizations and policymakers in creating focused initiatives.