Forestry inventories and mapping are vital elements of Sustainable Forest Management (SFM), supplying key data for the monitoring and conservation of forest ecosystems. Conventional forestry inventory techniques, although efficient, frequently encounter obstacles including labor-intensive data gathering, restricted spatial coverage, and measurement mistakes. The swift progression of geospatial technologies, such as remote sensing, LiDAR, and Unmanned Aerial Vehicles (UAVs), has markedly enhanced data collection and analysis, providing high-resolution insights into forest composition, biomass estimation, and land-use dynamics. Simultaneously, Artificial Intelligence (AI) has transformed forest management through the facilitation of automated data processing, predictive modeling, and decision-making assistance. Machine learning (ML) and deep learning (DL) methodologies enable land cover classification, species identification, and biomass calculation with unparalleled precision. AI-driven algorithms augment the analysis of LiDAR point clouds, satellite images, and hyperspectral data, minimizing the necessity for large field surveys while enhancing efficiency in forestry applications. This chapter examines the amalgamation of geospatial data with artificial intelligence in performing thorough forestry inventory and mapping. It analyzes how these technologies tackle significant obstacles in conventional forest monitoring, enhance the precision of biomass and carbon stock assessment, and facilitate biodiversity conservation. The chapter emphasizes recent advancements, including AI-driven object detection, feature segmentation, and real-time monitoring dashboards for sustainable forest resource management. Notwithstanding its benefits, GIS and AI-driven forestry applications encounter technological, socio-economic, and regulatory obstacles, such as data accessibility, computational intricacy, and the requirement for specialist skills. Confronting these difficulties necessitates interdisciplinary collaboration, investment in capacity-building efforts, and the establishment of standardized frameworks for data interchange.