<p>This study comprehensively reviews recent advances in the digital reconstruction of polycrystalline materials. Digital reconstruction serves as both a representative volume element for multiscale modelling and a source of quantitative data for microstructure characterisation. Three main types of digital reconstruction in polycrystalline materials exist: (i) experimental reconstruction, which links processing-structure-properties-performance by reconstructing actual polycrystalline microstructures using destructive or non-destructive methods; (ii) physics-based models, which replicate evolutionary processes to establish processing-structure linkages, including cellular automata, Monte Carlo, vertex/front tracking, level set, machine learning, and phase field methods; and (iii) geometry-based models, which create ensembles of statistically equivalent polycrystalline microstructures for structure-properties-performance linkages, using simplistic morphology, Voronoi tessellation, ellipsoid packing, texture synthesis, high-order, reduced-order, and machine learning methods. This work reviews the key features, procedures, advantages, and limitations of these methods, with a particular focus on their application in constructing processing-structure-properties-performance linkages. Finally, it summarises the conclusions, challenges, and future directions for digital reconstruction in polycrystalline materials within the framework of computational materials engineering.</p>