Zero-sum games (ZSGs) provide a robust control framework for autonomous systems operating underdisturbances. However, existing methods often face challenges in achieving real-time computation and maintaining high performance in unseen environments. Recent sampling-based approaches alleviate some of these issues but remain computationally inefficient, limiting robustness under real-time constraints. To address this, we propose ZSG-MPPI, a real-time robust control algorithm built on the Model-Predictive Path-Integral (MPPI) framework. By establishing a connection between ZSGs and optimal control problems (OCPs), our method leverages sampling-efficient MPPI techniques for OCPs while analytically computing the control policy for ZSGs. Simulation and hardware experiments demonstrate that our ZSG-MPPI achieves real-time performance and outperforms baselines in both robustness and sampling efficiency.