The forecasting of Foreign Direct Investment (FDI) flows is essential for governments and private entities that need to make informed strategic choices in today's dynamic global market. Standard econometric approaches like gravity equations and time-series models face difficulties in representing the complex nonlinear and high-dimensional relationships present in FDI determinants. This research investigates the use of various machine learning methods like Random Forests, Gradient Boosting Machines, Support Vector Machines, and Neural Networks to enhance the forecasting of FDI flows. The study examined the foundational theories of machine learning models, strengths and weaknesses, and capacity to improve forecasting accuracy and model interpretability. The research emphasized how combining domain expertise with transparent interpretable machine learning methods enhances decision-making and policy creation processes. This synthesis provides direction for upcoming empirical investigations and methodological decisions and strengthening the use of ML technology in forecasting Foreign Direct Investment (FDI) flows.