Macroeconomic forecasting remains difficult because aggregate dynamics are nonlinear, regime-dependent, and increasingly informed by high-dimensional, mixed-frequency data, conditions under which traditional econometric models often lose accuracy, especially around turning points. This study systematically reviews how artificial intelligence (AI) is being used to address these challenges and what reliable evidence exists on its forecasting value. Using a PRISMA-aligned protocol, we searched Web of Science, Scopus, and ScienceDirect, identifying 1,627 records; after de-duplication and multi-stage screening, 178 studies were retained for qualitative synthesis. The review develops an evidence-based taxonomy of AI model families in macroeconomic forecasting and synthesizes their motivations, performance patterns, and limitations across targets such as gross domestic product (GDP) growth, inflation, and labor-market indicators. This study shows that AI methods, particularly tree-based ensembles, deep sequence models, and hybrid econometric–AI systems, frequently improve short-horizon forecasting and nowcasting in data-rich settings and during crisis regimes, but do not dominate uniformly across variables or horizons. The study's contributions are threefold: a structured taxonomy that organizes a fragmented field, a consolidated assessment of where AI gains are robust versus conditional, and a clear articulation of the main methodological and policy-relevant constraints shaping future macro-AI forecasting research.
Mohammed et al. (2026) studied this question.