Over centuries, diverse construction techniques for historic masonry structures have emerged, shaped by geography, material availability, and craftsmanship. Despite their substantial impact on structural behaviour, masonry patterns remain underexplored due to the complexity of generating irregular patterns and simulating their structural response. Additionally, uncertainties in heritage structures—such as incomplete knowledge of original materials and techniques—complicate accurate safety assessments. This study addresses these challenges by proposing a novel assessment framework that utilises artificial pattern generation and geometric quality indexes (QI-s) to minimise survey intrusiveness while maximising prediction accuracy. The framework introduces QI-s to quantify pattern regularity, incorporates algorithms for generating diverse masonry patterns tailored for block-based simulations, and integrates these with structural analysis methods that balance computational efficiency and accuracy. The framework is validated against a synthetic benchmark, demonstrating its effectiveness in characterising masonry structures and assessing their behaviour. By enabling a more accurate yet less intrusive evaluation of heritage masonry, this approach advances current methodologies for the structural assessment of historic masonry structures.