This study presents a novel approach to real-time wind tunnel data reduction by integrating a JR3 six-axis force-torque sensor with machine learning algorithms. Traditional aerodynamic testing often involves large volumes of raw data from force balances, which require extensive post-processing. This paper proposes a machine learning-based model that accelerates the data reduction pipeline, allowing for near-instantaneous derivation of aerodynamic coefficients from JR3 balance data. The framework includes a synchronized data acquisition module, signal preprocessing, a trained regression model, and an interactive visualization tool. Results show that the proposed system can achieve real-time performance while maintaining high accuracy, significantly reducing the computational and time costs associated with wind tunnel testing.