The early detection of macroeconomic crises has become a critical area of focus in financial and economic research, particularly due to the increasing complexity and interconnectedness of global markets. Traditional econometric models which mostly rely on prespecified relationships and assumptions, while foundational, often struggle to capture the rapid shifts and nonlinear dynamics characteristic of modern financial systems, leading to delayed or less accurate crisis predictions. This systematic review explores the potential of machine learning (ML) as a robust alternative for early warning systems (EWS) capable of addressing these limitations. ML models such as neural networks, support vector machines, and ensemble models show promising improvements in predictive accuracy, adaptability, and scalability, effectively handling vast and diverse datasets. These capabilities enable ML-based models to identify early indicators of systemic risk across global financial networks, supporting proactive responses to emerging crises. However, challenges persist, particularly around the interpretability and consistency of ML models, which can affect stakeholder trust and complicate applications in regulatory and policy settings. Further research into integrating interpretability and addressing data quality concerns could enhance the role of ML in creating reliable, policy-ready early warning frameworks. The findings underscore ML’s potential to supplement or transform traditional approaches to economic stability, providing tools to support more timely and accurate financial risk assessment and crisis mitigation strategies.