Abstract Multimodal data and machine learning techniques have emerged as a transformative approach in 3D geospatial modelling, offering enhanced precision and comprehensive spatial understanding. However, existing studies focus on single data sources or conventional machine learning techniques without systematically evaluating how multimodal integration enhances 3D geospatial modelling. This systematic literature review assesses the integration of multimodal data with machine learning techniques in 3D geospatial modelling. Using the PRISMA framework, literature published up to January 2025 from Scopus and Web of Science databases was reviewed, resulting in 26 peer-reviewed articles. The review highlighted key data modalities, including point clouds, remote sensing imagery, imaging data, GIS data layers, and non-spatial data. Further, various machine learning methods were assessed, including traditional approaches, deep learning techniques, and hybrid fusion models. This study was analysed by synthesising insights on data fusion techniques, evaluating their strengths, weaknesses, and applicability across diverse geospatial contexts. Major challenges identified include data heterogeneity, computational demands, and limited model generalisability. This review provides future research directions, emphasising opportunities for methodological advancement, enhanced interoperability, and scalable artificial intelligence frameworks suitable for diverse applications, such as urban development, environmental monitoring, precision agriculture, and disaster management.
Mohamad et al. (2026) studied this question.