Sunn pest (Eurygaster spp.) is one of the most important pests adversely affecting the yield and quality of wheat, which is one of the main nutritional products for humans. In controlling the sunn pest, determining the migration pattern of the pest from its overwintering sites to wheat fields is of critical importance. The ability to predict the onset and end of this migration event forms the basis of forecasting and warning systems for controlling the sunn pest. In this study, conducted over four life cycles from 2014 to 2018 in two wintering sites, the predictability of the onset and end of the sunn pest's migration from wintering sites to wheat fields using temperature data was investigated. The obtained data were evaluated using pure temperature values, the day-degree model using effective temperature sums, and machine learning (decision tree) methods using cumulative temperature values. The study revealed that the migration pattern of the sunn pest cannot be explained solely by temperature data.