The framework of heritage conservation is extremely complex due to the historical stratification, territorial extension, and heterogeneity of such a context. It is not possible to study and preserve landscapes without a thorough knowledge of the geographic relationships that landscapes, as cultural and environmental heritage, establish with the territory they occupy and the people who inhabit them. Given this delicate context, Cultural Heritage (CH) conservation needs efficient and smart data acquisition, structuring, and management methodologies. This is particularly evident when dealing with extended and diffuse heritage architectures or landscapes. In this context, Remote Sensing (RS) data, GeoAI, and specifically Machine Learning (ML) methodologies represent powerful tools to enhance the automation of documentation processes. This contribution aims to provide an overview of the evolving possibilities of artificial intelligence (AI) techniques that can be applied to geospatial data. ML methodologies are, in fact, increasingly multidisciplinary and have been widely used in several CH mapping and analysis applications. One of the research aims is to demonstrate the effectiveness of these tools for the Historical Military Built and Landscape context. The research studied a methodological pipeline for the Military Heritage documentation along the coast of the Sardinia region, where the conservation of extensive, widespread, and heterogeneous defensive landscapes is a critical issue shared by researchers and public administrations. Specifically, Sardinia’s historical military systems and architectures represent a notable example and a significant case study, both for their chronological and typological variety and for the widespread distribution and number of assets. This paper thus aims to test GeoAI methodologies for airborne Light Detection and Ranging (LiDAR) data to address the mapping and data structuring challenges of such widespread Heritage. Specifically, deep learning (DL) point cloud semantic segmentation, classification, and object detection techniques represent a valuable opportunity to reduce the human operator time resource consumption. Correspondingly, the leverage of subsequent Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) for object-based image analysis is a well-known tool for heritage asset mapping and detection. Finally, the employed available techniques are critically evaluated, in order to estimate their potential and establish a possible methodological pipeline suitable for extensive landscape and built heritage contexts, supporting their preservation.