With the rise of digital gaming, questions about game addiction and their mental health impact are becoming more widespread in Bangladesh. An Empirical Study of Game Addiction in Bangladesh via five machine learning algorithms based on Linear Regression, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors (KNN), and Naive Bayes View PDF Complete Researcher Research Grants and other Funding Opportunities Research Grant Awards Editor-in-Charge Cite Entry Block Cite Entry In our case, Linear Regression provides a modest prediction ability (MAE: 0.1181, MSE: 0.0249). The three of the classifiers that performed best were Logistic Regression, Naive Bayes, Decision Tree Classifier, all of which achieved 100% accuracy in identifying effective patterns of addiction. KNN is promising (83%) but also hints at some tuning should also be done in this context. This study gives idea on how various algorithms perform for detecting addiction and a talk on need based model selection for proper analysis. Such findings have implications for the design of focused interventions and prevention strategies but should also stimulate future research that considers the local context of the data.