This article analyzes the application of dynamic econometric models in economics, particularly the contemporary trends observed in recent years. These models (for example, DSGE, VAR, and dynamic panel models) serve as primary tools for evaluating economic dynamics, shock impacts, and policy assessment, with their precision being enhanced through the integration of machine learning (ML) and big data. The methodology employs a systematic literature review, involving the analysis of several research studies from prestigious journals. The results demonstrate that DSGE models provide macroeconomic forecasts with RMSE values ranging from 0.15 to 0.25, VAR models assess shock impacts, and ML integration improves accuracy by 20-25%, although limitations exist due to computational complexity and data uncertainty. In conclusion, the role of these models in shaping economic policy is emphasized, with recommendations provided for deepening ML integration, expanding the utilization of big data, and addressing the aforementioned constraints.