The aim of this study was to examine mathematically gifted students' learning styles through data mining method. 'Learning Style Inventory' and 'Multiple Intelligences Scale' were used to collect data. The sample included 234 mathematically gifted middle school students. The construct decision tree was examined predicting mathematically gifted students' learning styles according to their multiple intelligences and gender and grade level. Results showed that all the variables used in the study had a significant effect on mathematically gifted students' learning styles, but the most effective attribute found was intelligence type.