Migration Control of Persons as a Process Influenced by Generative AI: An Exhaustive Systematic Review

Abstract

In recent years, the use of generative artificial intelligence in migration control has increased steadily, driven by the need to optimize verification, security, and predictive analysis in border contexts. The objective of this paper is to determine the state of the art of research on Generative AI and its impact on Migration Control of Persons. A systematic and bibliometric review was conducted on 61 papers published between 2019 and July 2025 in IEEE Xplore, Scopus, Web of Science, ARDI, and Wiley Online Library. The studies are concentrated in Q1–Q2 journals and prioritize machine learning approaches applied to border surveillance, digital borders, and automated classification. The results reveal the centrality of topics such as “machine learning,” “artificial intelligence,” “border control,” and “data protection,” as well as the presence of highly specialized themes (Border Classification) and others that are marginal yet emerging (Mobility Analytics, AI Data Migration). Overall, the findings describe a methodologically robust field that is predominantly technocentric and security-oriented, with limited explicit consideration of human rights and Global South perspectives. It is concluded that advancing toward more transparent and accountable migration control requires aligning Generative AI with algorithmic governance frameworks and empirical assessments in real-world environments.