Permafrost maps are important for regional planning in cold regions, but binary products do not represent gradients in thermal conditions, seasonal thaw response, or mapping confidence. This study developed an uncertainty-aware regional permafrost-condition screening framework for the Daxing’anling region during 2003–2022 by integrating TTOP-derived mean annual ground temperature (MAGT), reconstructed permafrost-occurrence probability, and Kudryavtsev-model-derived active-layer thickness (ALT). MAGT ranged from −4.11 to 5.28 °C, with a mean of −0.28 °C, and comparison with 23 published borehole records from 17 reported locations yielded r = 0.756 and RMSE = 0.419 °C. Explicit measurement depths were available for 12 of the 23 records and ranged from 10 to 15 m. Model-derived ALT ranged from 1.480 to 1.864 m. The complete point-scale evaluation using all 12 valid maximum depth of seasonal thaw (MDST) records yielded an RMSE of 0.92 m. Adding ALT changed 21.05% of valid-pixel assignments. Cold–low-response permafrost, Cold–moderate-response permafrost, Warm–enhanced-response permafrost, Near-thaw transitional permafrost, Marginal/low-confidence permafrost, and Non-permafrost occupied 7.3%, 17.1%, 14.9%, 4.1%, 13.9%, and 42.8% of the domain, respectively. Full Monte Carlo uncertainty propagation retained 78.65% modal agreement with the deterministic baseline, and 56.65% of the domain had a maximum class-membership probability below 0.60, whereas threshold-only perturbation retained 98.92% agreement. The framework is therefore suited to regional investigation and monitoring prioritization; project-level engineering decisions require direct geotechnical and deformation-based evidence.