Operational weather forecasting in Switzerland currently lacks targeted diagnostic tools for severe convective gusts, leaving their occurrence and detailed physical properties insufficiently quantified. In this study, we perform case studies of wind gust-producing convective cells to link 3D C-band polarimetric radar signatures and lightning with gust occurrence. We find that reflectivity cores, <em>K</em>_DP cores, and mid-altitude radial convergence are linked to surface wind gusts. These associations informed the development of the Burstinator, a Random Forest machine-learning model designed to distinguish between thunderstorms with and without severe gusts. The Burstinator yields a 0.66 detection probability paired with a false alarm rate of 0.08, demonstrating superior skill over the baseline WDRAFT. Predictor importance analysis indicates that radar-based features are the top predictors. Finally, we apply the Burstinator to a two-year dataset of 5-min resolution radar data to characterize the spatial distribution of the convective gust frequency. Our findings highlight how integrating machine learning with dual polarization radar and lightning observations can advance convective wind detection and severe weather monitoring across Switzerland.