Chilean temperate forests are dominated by Nothofagus and Araucaria araucana species. Despite A. araucana not being at imminent risk of extinction, its cultural value and the associated environmental services and landscape goods have an important role for the conservation of this native forest. In some areas, the future conservation of A. araucana is a cause of great concern given its management prohibition and regeneration limitation due to slow growth, canopy tree competition and dense understory. The above characteristics make this species most susceptible to some disturbances, such as livestock, wildlife and human pressures. Therefore, sustainable management of A. araucana forests requires the assessment of its regeneration condition. The objective of this research was to apply multivariable analysis techniques in search of the most relevant parameter for Araucaria regeneration. This study used the following methods: principal component analysis (PCA), forward stepwise regression modeling and Maxent modeling. By PCA, it was possible to reduce the dimension to six-dimensional with a variance explanation of greater than 75%. The multivariable regression model, known as model 7, was the best compromise between the coefficient of determination and model size (number of independent variables). Incorporating a maximum entropy trend improved model performance. A spatial prediction was obtained by summing the contributions of statistical methods and the geographic information system (GIS). The GIS increased the flexibility of the proposed model, which enabled an extrapolation to other areas at different spatial and temporal scales.