Forest management planning requires a permanent collection of data on the distribution, composition, and structure of the stands that conform a woodland. These data serve as the basis for suggesting the most appropriate management scheme according to the natural resource conditions and management objectives. It is common for the collected databases' structure and dimension to hinder their analysis using traditional descriptive techniques. Therefore, alternative methodologies are required to facilitate both the exploration of data properties and their collective behavior. We used complex networks analysis to identify distribution patterns of topographic, biological, and productive conditions of a managed forest, suggesting its functional zoning. The forest was considered a graph consisting of nodes and edges; the stands served as nodes and interactions between them as edges. Degree, clustering coefficient, triangles, and modularity were used as segregation and connectivity metrics to evaluate forest properties and allocate stands to five predefined potential forest uses (zones). The clustering coefficient metric provided the better graph partition, allowing to obtain the best alternatives for zoning the forest in conservation areas, areas with potential for timber production, and carbon storage. Proposing forest functional zoning through complex network theory is a powerful methodological option to represent the spatial and nonspatial interactions among the relevant attributes defining a forest ecosystem condition.