Human migrationHuman migration is a significant global phenomenon that affects countries of origin, destination, and their surrounding regions. Despite its impact, migration has historically been a challenge to predict reliably. In this paper, we examine the feasibility of applying machine learningMachine learning to predict migration trends using historical data and various socioeconomic indicatorsSocioeconomic indicators. Two engineered indices were developed to assess the interdependencies of these factors. Among the machine learningMachine learning models tested for binary directional prediction, extreme gradient boosted trees (XGBoost)XGBoost demonstrated the highest accuracy. The XGBoost model achieved an average directional prediction accuracy of 93.32% for emigration and 89.94% for immigration across 173 countries, highlighting the potential of machine learningMachine learning to enhance global migration forecastingMigration forecasting.