The complexity of real-world applications of biomass energy has increased substantially due to so many competing factors. There is an ongoing discussion on biomass as a renewable energy source and its cumulative impact on the environment vis-a-vis water competition, environmental pollution and so on. This discussion is coming at a time when evolutionary algorithms and its hybrid forms are gaining traction in several applications. In the last decade, evolution algorithms and its hybrid forms have evolved as a significant optimization and prediction technique due to its flexible characteristics and robust behaviour. It is very efficient means of solving complex global optimization problems. This article presents the state-of-the-art review of different types of evolutionary algorithms, which have been applied in the prediction of major properties of biomass such as elemental compositions and heating values. The governing principles, applications, merits, and challenges associated with this technique are elaborated. The future directions of the research on biomass properties prediction are discussed.