Advanced 3D imaging techniques and image segmentation and classification methods can profoundly transform biomedical research by offering deep insights into the cytoarchitecture of the human brain in relation to pathological conditions. Here, we propose a comprehensive pipeline for performing 3D imaging and automated quantitative cellular phenotyping on Formalin-Fixed Paraffin-Embedded (FFPE) human brain specimens, a valuable yet underutilized resource. We exploited the versatility of our method by applying it to different human specimens from both adult and pediatric, normal and abnormal brain regions. Quantitative data on neuronal volume, ellipticity, local density, and spatial clustering level were obtained from a machine learning-based analysis of the 3D cytoarchitectural organization of cells identified by different molecular markers in two subjects with malformations of cortical development (MCD). This approach will grant access to a wide range of physiological and pathological paraffin-embedded clinical specimens, allowing for volumetric imaging and quantitative analysis of human brain samples at cellular resolution. Possible genotype-phenotype correlations can be unveiled, providing new insights into the pathogenesis of various brain diseases and enlarging treatment opportunities.