Climate change directly or indirectly affects forest growth and productivity, disturbing the plant’s physiological processes, composition and distribution patterns. Due to climate change, significant forest changes have been observed in the last half-century. Climatic conditions such as temperature and precipitation are closely related to forest growth and distribution. Also, climatic conditions are commonly interpreted to observe the response of forests to the changing climate. Therefore, there is some mutual relation between climate change and forest dynamics, which need to be critically investigated for sustainable forest management and climate change mitigation. This chapter discusses the studies that highlight the benefits of integrating machine learning algorithms to the study of forest growth and distribution. Machine learning and remotely sensed datasets unlock new opportunities to study the forest cover dynamics and distribution mapping at a large scale, with faster speed and accuracy.