Acta Electronica Malaysia (AEM)

The increasing complexity and volatility of global markets necessitate advanced tools for accurate economic forecasting. This study explores the synergy between mathematical modeling and machine learning as a hybrid approach to predicting market trends. By leveraging the analytical rigor of mathematical models alongside the adaptability and data-driven insights of machine learning algorithms, this research offers a robust framework for understanding and anticipating economic fluctuations. The integration of these techniques enables the capture of both deterministic economic principles and nonlinear, high-dimensional patterns inherent in financial data. The hybrid approach enhances the precision and reliability of forecasts by accommodating diverse variables and rapidly evolving market dynamics. It also supports the identification of latent relationships and emerging economic indicators that traditional models may overlook. This framework is particularly valuable for policy analysts, investors, and financial institutions seeking to make informed decisions in an increasingly digitized and data-intensive environment. The paper emphasizes the growing relevance of interdisciplinary solutions that merge quantitative rigor with intelligent automation. Ultimately, the findings underscore the transformative potential of combining mathematical and machine learning paradigms in economic forecasting, fostering greater resilience and responsiveness in economic planning and strategic investment. This work contributes to ongoing discourse on predictive analytics in economics, offering pathways for more proactive and informed market engagement.