The expected result of this research is the development of a comprehensive and structured 3D representation of university building spaces that accurately reflects their physical, functional, and administrative characteristics. The model will provide a classified and data-rich spatial environment in which each space is uniquely identified and enriched with standardized attributes, enabling a consistent understanding of the current state of campus facilities. Another expected outcome is the establishment of a centralized and reliable spatial data structure that functions as a single source of truth for space-related information. By organizing spatial and usage data within a coherent framework, the research aims to reduce inconsistencies, duplication, and fragmentation of information that often exist across departments. The study also expects to demonstrate how enhanced visualization and structured data modeling can support space usage monitoring and performance analysis. Through improved transparency and clarity, decision-makers will be able to better evaluate allocation patterns, identify underutilized or misclassified spaces, and align space distribution with institutional needs. In addition, by synthesizing space management initiatives from universities worldwide, the research is expected to provide benchmarking insights and highlight practical directions for improving current practices. Overall, the anticipated results include improved data reliability, clearer spatial understanding, strengthened monitoring capabilities, and more informed, evidence-based decision-making in university space management.