The growing integration of Digital Pathology (DP) within histological analysis has opened new perspectives in biomedical imaging and data-driven research. Whole Slide Imaging (WSI) technologies enable large-scale digitalization of tissue samples, allowing visualization, storage and computational processing. However, the development of reliable data-driven methods remains limited by the lack of annotated dataset and especially due to the high inter-operator variability in the labelling process performed by pathologists. To address these challenges, this thesis proposes to invert the traditional paradigm by introducing a controlled generation framework for synthetic histological images with user-defined semantic content. The proposed system called HISPACE (Histological Image Synthesis with Pattern And Content Engine) is based on a hybrid pipeline, where semantic content generation is driven by explicit mathematical modelling, while the subsequent translation into photorealistic images is achieved through a GAN model.