Uncertainty-aware machine learning-based prediction of plasma parameters in a microwave atmospheric pressure plasma jet

values exceeding 0.97 across all plasma parameters. Bootstrap-based uncertainty quantification demonstrated near-nominal 90% prediction interval coverage with comparatively narrow uncertainty bounds, and calibration analysis confirmed statistically consistent uncertainty estimates. Experimental validation using 30 independent plasma operating conditions, separated into interpolated and extrapolated regimes, further confirmed robust generalization, with increased epistemic uncertainty appropriately accompanying extrapolative predictions. SHapley Additive exPlanations (SHAP) based interpretability analysis identified microwave power as the dominant controlling feature for most plasma parameters, while gas flow rate governed the intensity of OH emission. Overall, this uncertainty-aware ML framework provides a reliable foundation for data-driven plasma diagnostics and future optimization of MW-APPJ-based processes.