The optimisation of massive data obtained from 3D acquisition methodologies through AI represents an innovative research frontier in 3D data management. It arises from the ever-increasing instruments’ capacity to acquire enormous amounts of geometric and radiometric information with a substantial increase in processing times, a demand for computing capacities, and the request to subsample ultra-dense point clouds at the end of the process. On the contrary, a priori intervention on the raw data can mitigate the role of data dimension, reducing processing times while preserving the valuable information to analyse and interpret the artefacts. The research presents a new methodological approach based on integrating photogrammetry and AI. Through AI algorithms, it was possible to optimise the weight of the images, automatically cluster and segment image areas, and assign different resolutions according to the image content. This experimental pipeline significantly reduced calculation times, extracted point clouds with variable resolution according to the elements represented, and preserved the architectural artefacts’ geometry.