<p>Five-axis free-form machining requires high form accuracy and productivity simultaneously, yet digital twins deployed in industry often lose predictive accuracy when tool paths, workpieces, or controller settings change. This paper presents an AI-driven framework that performs ROI-guided digital twin self-calibration, uncertainty-aware composite risk mapping, and cycle-time invariant NC data optimization. A single dry-run execution is used solely as the calibration and optimization input, whereas the resulting NC patches are validated through representative finish-cut machining experiments rather than by dry-run reproduction alone. We calibrate a grey-box feed-drive model with nonlinear friction via hierarchical Bayesian inference, combining parametric corrections with a surface-parameterized Gaussian process residual that also quantifies predictive uncertainty. The calibrated posterior is propagated to construct a composite risk map that fuses predicted form error, predictive variance, and jerk-saturation proximity, from which vulnerable regions are automatically extracted. Guided by these regions, a constrained optimization redistributes feedrate to reduce risk while enforcing a cycle-time invariant (non-increasing, controller-reported) constraint and a global improvement condition, yielding vendor-neutral NC patches that modify only feedrate commands. Experiments include finish-cut machining of a turbine-blade mold cavity on a custom PC-based five-axis testbed, with supplementary dry-run tests on a commercial CNC. The ROI-guided calibration improves local prediction accuracy and risk-map reliability. The resulting vendor-neutral NC patch reduces peak and mean form errors by 65.2% (28.2 → 9.8&#xa0;μm) and 25.6% (3.9 → 2.9&#xa0;μm), respectively, improves surface quality, and keeps the controller-reported cycle-time essentially unchanged.</p>