Process capability indices are widely used in manufacturing quality control, but capability approval is often implemented by directly thresholding finite-sample estimates, which can produce unstable and poorly calibrated decisions near the approval boundary. This paper develops a hybrid statistical--learning framework for capability-based decision support in manufacturing. The proposed UC-Cap approach combines a statistically grounded capability baseline with a residual learning component that uses process, distributional, specification-related, and measurement-related features to refine capability-decision risk estimates under non-ideal manufacturing conditions. The statistical baseline preserves the interpretability of classical capability analysis, while the learning component provides data-driven correction for systematic deviations arising from non-normality, measurement effects, and finite-sample variability. A nested Monte Carlo evaluation is introduced to assess probabilistic calibration under controlled synthetic settings, and an empirical manufacturing study is used to examine decision behavior under realistic capability data. Results show that deterministic thresholding can lead to substantial instability and miscalibration in near-boundary regimes, whereas the proposed framework provides calibrated risk estimates, interpretable decision outputs, and improved support for capability approval decisions. The framework is compatible with existing capability-analysis workflows and can be integrated into manufacturing quality decision-support systems.

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