Big Data and Migration Forecasting: Predictive Insights into Displacement Patterns Triggered by Climate Change and Armed Conflict

Abstract

Data-driven strategies are becoming more and more important for improving humanitarian planning and migration governance according to time-series analysis, machine learning techniques, and demographic segmentation. The use of predictive analytics and big data in anticipating migration trends motivated by environmental change and armed conflict was examined in this work. A continuous increase was observed in both climate-induced and conflict-induced displacement from 2022 to 2024; conflict-related displacement greatly exceeded earlier levels. Although migration brought on by conflict mostly affected the Middle East and South America, Asia and Africa show more displacement related to climate conditions, but geographical differences were executed. These results highlighted the need for regionally and contextually specific treatments. Machine learning models, especially LSTM and XGBoost were better than conventional techniques including ARIMA in forecasting accuracy, but much reduced in MAE and RMSE values. This helps advanced predictive modeling techniques for population migration to be integrated. Emphasizing demographic impact, it showed that the most displaced group consists of people between the ages of 25 and 54, therefore stressing the mobility and economic activities of this cohort. Still, children and the elderly showed less displacement, who suffer more during crises. The importance of integrated early warning systems since it showed quite strong relationships between displacement levels and rising conflict indices. These realizations highlighted how predictive technologies are necessary for best resource allocation, proactive migration control, and direction in humanitarian reactions. To improve world displacement readiness, the study advocated scalable, inclusive, and ethical forecasting approaches.