Intelligent Modeling of African Migration: Gendered Insights from 1990 to 2050 Using Computational Prediction

Abstract

The international migration is still a significant world level demographic factor with far-reaching socio–economic policy and planning consequences. We use sophisticated approach in the computational social sciences to predict the gendered patterns of migration among five regions of Africa: Northern, Southern, Eastern, Western, and Middle Africa for 2030-2050 years, based on the 2024 United Nations Population Division International Migrant Stock data set. This emphasis is warranted by dynamism in population changes on the continent, which is due to a rapidly growing young population and its position as a source and destination of international migration. These trends in the region are critical to analyze in terms of socio-political, economic, and environmental factors that influence mobility. Based on the sex-disaggregated data, this study adopts Linear Regression, Random Forest and Support Vector Regression models to forecast future migrant numbers in 2030 and 2050. This work presents an ML approach to demographic analysis to achieve a region-wise estimation of gender wise migration and presents an approach to delivering micro-level evidence-based policies to be used as a foundation of targeted policy making and strategic management of migration flows. By combining the angle countrywide and gender wise, these insights reinforce computational demography and provide an important instrument for proactively planning humanitarian and development action on the African continent.