Overindulgence in social media use is increasingly becoming a matter of concern amongst students and is normally accompanied by behavioural, academic and health implications. The purpose of this study is to measure the rates of social media addiction among students and define the most important factors influencing it by means of data-driven methods. The analysis was done with a dataset of 705 student responses to determine the trends in addiction and forecast the level of addiction using machine learning models. The descriptive analysis indicated that 57.87 percent of students were in the High addiction category with the results being equal among all genders. Correlation analysis revealed that the greater the addiction scores, the greater the negative academic impact and the interpersonal conflicts, and conversely, the greater the influence on the sleep duration and mental health. The classification using several machine learning models was done, with K-Nearest Neighbours (KNN) and CatBoost having the greatest accuracy of 98.6 percent. To assess model robustness, the optimized KNN model was further validated using ten-fold cross-validation, achieving a mean accuracy of 97.17% (95% CI: 95.52%–98.81%). The optimized KNN model demonstrated stable predictive performance across different data partitions. SHAP-based explainability identified average daily social media usage, mental health score, sleep duration and conflicts over social media as the most influential predictors of social media addiction. These results indicate the necessity of awareness and careful intervention approaches and show the potential of machine learning to avert early social media addiction in students.