Researching the predictors of gambling among adolescents is important, especially as risk factors become more and more common across countries. Doing cross-national studies is also important because when thinking about solutions, the same solution cannot be applied to every country as every country has different predictors that indicate a risk for problematic gambling. The use of a machine learning model was especially important in this study because it can handle the large number of predictors in this study. It also made the analysis of the data much easier as normal statistical models would not be able to handle this type of data and be able to pick out which predictor was the strongest. The machine learning model also made cross-country comparisons easier because it can test whether certain variables can consistently predict gambling or if the variables varied across countries. Machine learning models should be encouraged in future studies of this nature because they can handle many variables at once and can handle complex, multinational datasets.