Managing forests requires an understanding of spatial patterns in the distribution of different species. However, species distributions are strongly influenced by variations in current and future climatic conditions. Species distribution models (SDMs) are usually used to determine current and potential distribution ranges, about multiple environmental factors that help to compare the changes/shifts in patterns of distribution under different climate change scenarios. SDMs often involve integrating field-sampled data with remotely sensed observations to generate prediction maps. Several models aid in predicting species distribution like generalized linear models (GLM), generalized additive models, random forests, maximum entropy (MaxEnt), artificial neural networks, etc. SDMs are a useful tool in forest management as they help in predicting tree occurrences, disease outbreaks, invasion zones, etc. This review focuses on the different applications of SDMs in forest management, constraints, and potential directions to avoid possible pitfalls.