Volatility in tax policy presents significant challenges for governments, businesses, and investors, particularly in uncertain environments shaped by geopolitical shifts, technological disruptions, and macroeconomic instability. This study develops and evaluates scenario modeling frameworks designed to predict and manage tax policy volatility, enabling more robust fiscal planning and strategic decision-making. Drawing on theoretical and empirical foundations in public finance, policy modeling, and uncertainty analysis, the study integrates econometric modeling, Monte Carlo simulations, and dynamic stochastic general equilibrium (DSGE) frameworks with qualitative scenario planning techniques. Results highlight the utility of hybrid approaches that combine quantitative rigor with contextual adaptability. The findings suggest that scenario-based models can not only anticipate potential policy shifts but also inform adaptive responses to mitigate adverse impacts on revenue mobilization, equity, and economic growth. The paper concludes with a proposed evidence-based framework for applying scenario modeling in tax policy design, offering practical insights for policymakers, international institutions, and private sector stakeholders seeking resilience against policy shocks.