<p>Mineral resource classification, based on confidence levels associated with tonnages and grades, is essential for public disclosure and for assessing deposit maturity and related risks. Although geometric classification methods are transparent and straightforward, they do not explicitly account for local variations in uncertainty. In contrast, geostatistical approaches focus primarily on the quality of grade estimation. The scorecard methodology attempts to integrate multiple parameters relevant to mineral resource classification; however, elements such as the definition of uncertainty classes and the weighting of individual criteria often rely on subjective judgment. This study proposes a hybrid methodology for mineral resource classification that integrates traditional geometric approaches with a quantitative risk map. The risk map incorporates three main sources of uncertainty: (1) geological uncertainty, (2) database quality, and (3) grade estimation quality. To minimize subjectivity in the integration of these factors, the risk map is constructed using a <i>k</i>-means clustering approach, resulting in three risk categories: low, medium, and high. The final classification is obtained through a hierarchical combination of geometric classification and the risk map. A machine learning post-processing step is applied to mitigate local classification artifacts, commonly referred to as “salt-and-pepper” or “spotted dog” effects. The proposed methodology was applied to a stratigraphic deposit case study and yielded robust results. It effectively integrates conventional geometric classification with key sources of uncertainty, reduces subjectivity compared with traditional scorecard methodology, and ensures reproducibility in mineral resource classification.</p>