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The forest is considered as a significant source of woody biomass production. Sustainable production of wood, lower emittance of CO2 from burning, and lower amount of sulfur and heavy metals are the advantages of woods rather than fossil fuels. The utilization of biomass, as an energy resource, is required four main steps of production, pretreatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the production of the woody biomass raw material in forests because investigating numerous related works concluded that there is a considerable reviewing gap in analyzing and collecting the applications of Machine Learning in the woody biomass. To fill this gap in the current work, the origin of woods is explained and the application of Machine Learning in this section is scrutinized. Then, the multidisciplinary enhancement approaches in the production of plants as well as the role of Machine Learning in each of them are reviewed. Meanwhile, the role of natural and planted forests in the production of woody biomass is explained and the application of Machine Learning in these areas is surveyed. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning is widely utilized in the production of woody biomass to enhance the wood production quantity and quality, improve the predictions, enhance the harvesting techniques, and diminish the losses.

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