Examining the Effective Role of Artificial Intelligence in the Interconnected Crisis of Climate Change and Human Migration

Abstract

Introduction: Climate change is a key driver of human migration, particularly in regions facing resource scarcity and extreme weather events. Understanding migration patterns is essential for effective policy responses. Objectives: This multidisciplinary study applies data mining techniques to identify key environmental and socioeconomic factors influencing climate-induced migration and enhance predictive modeling for policy decision-making. Methods: Machine learning techniques, including spatiotemporal clustering and regression analysis, are applied to migration data from UNDESA and IOM’s CLIMB Database. Climate indicators such as temperature anomalies, drought frequency, and water stress are analyzed. Results: Findings reveal strong correlations between climate stressors and migration trends. Water scarcity and prolonged droughts significantly drive displacement, with predictive models demonstrating high accuracy in forecasting migration flows. Conclusions: Data mining is a valuable tool for analyzing and predicting climate-induced migration. Findings emphasize the need for proactive climate adaptation strategies and data-driven migration policies. Future research should integrate real-time monitoring and geospatial AI to improve forecasting accuracy.