arXiv:2611.13893v1 Announce Type: new Abstract: Artificial intelligence and machine learning (AI/ML)-based regression models support operational decision-making in optical networks but typically expose only point predictions, which carry no statistically controlled information about their reliability. This paper proposes a post-hoc conformal risk control (CRC) framework that keeps a pretrained AI/ML regressor fixed, provides a finite-sample guarantee on uncertainty band miscoverage, and maps the band to operational decisions through policy rules. The guarantee applies to interval miscoverage, while decision-error rates are evaluated empirically. We instantiate the framework for quality of transmission (QoT)-aware lightpath (LP) feasibility assessment, in which a regressor predicts optical signal-to-noise ratio (OSNR) under domain shifts caused by fiber, amplifier, and switching upgrades. Feature shift contribution scores identify the LP features whose relation to QoT changes the most across a technology transition, and two policies translate the bands into feasibility decisions, allowing operators to tune the trade-off between conservatively rejecting or aggressively accepting LP realizations. Experiments on open space-division multiplexing (SDM) QoT datasets over the CONUS topology show that, for a target coverage of 90%, the uncertainty bands meet the target in all 170 trials spanning 17 domain shifts and ten calibration-test partitions, whereas a source-only quantile regression (QR) baseline meets it in none. Under severe shifts, the efficiency-oriented (EO) policy empirically improves zero-shot (ZS) feasibility-decision accuracy from about 85% to above 99%, approaching a transfer learning (TL) policy while avoiding retraining. The framework provides finite-sample control of uncertainty band miscoverage for regression-based optical network decision support, with potential applications beyond QoT-aware LP feasibility assessment.