 Growth prediction technology has become increasingly important for ensuring the safety of image processing techniques. It can be used to supplement the growth images obtained from the original images, especially when there are insufficient datasets. This makes it possible to increase the robustness of machine learning algorithms, which can then be applied to a wide range of applications. Furthermore, this technology can be used to predict the growth of living organisms, which can further extend its use into unexplored areas. A systematic literature review was conducted to investigate the current trends in biological growth prediction. The results were published in 47 papers between 2017 and 2022, with 20 papers focusing on machine learning. These papers focused on using LSTM, JAN, and STN as the most commonly used methods. This article was authored by Yojiro Hairi, Bishnu Prasad Gautam and Katsumi Wasaki.