The present paper will discuss the ways in which the application of the artificial intelligence (AI)-based predictive models may be employed to investigate the determinants of foreign direct investment (FDI) in emerging economies with a particular focus on the promotion of the United Nations Sustainable Development Goals (SDG 17: Partnerships for the Goals; SDG 10: Reduced Inequalities). Technology transfer and economic integration as a form of capital inflows has played a major role in the developing regions, yet the conventional econometric models are failing to account for investment decisions in a nonlinear and nondimensional manner. This paper employs AI tools, machine learning algorithms, and the possibility of natural language processing of policy texts, neural networks to predict future macroeconomic developments to establish key factors that affect FDI flows, including political stability, the quality of infrastructure, the market potential, and the effectiveness of the institution. The findings suggest that AI-enhanced models are significantly more effective in terms of forecasting the correctness of FDI patterns, compared to the traditional approaches, governance, and social inclusion have been reported to mediate. The article postulates that AI application in FDI analysis does not only assist in improving evidence-based policymaking but also more equal distribution of investment benefits across emerging economies and, thus, establish a global partnership and curb structural imbalance.