Mathematical modeling plays a vital role in economics and finance by providing analytical tools for prediction, risk management, and policy evaluation. This study develops a comprehensive framework that integrates econometric methods, stochastic modeling, dynamic systems, and agent-based simulations for predictive analysis and policy optimization. Using simulated macroeconomic data from 2010–2024, the study evaluates policy scenarios involving fiscal stimulus, tax adjustments, and monetary easing. The results demonstrate that hybrid models significantly improve prediction accuracy (by 12–18%) and policy efficiency (reducing unemployment by up to 25% compared to baseline). The framework emphasizes robustness, adaptability, and interpretability, offering policymakers and analysts a structured approach to navigating complex economic systems.