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In this study, we refine in vitro propagation techniques for Camellia sinensis using a machine learning approach to ascertain the influence of different shooting and rooting conditions on key growth metrics. This was achieved by applying random forest (RF), XGBoost, and multilayer perceptron (MLP) models to dissect the complexities of micropropagation and rooting processes. The research unveiled significant disparities in growth metrics under varying media conditions, underscoring the profound impact of media composition on plant development. The meticulous statistical analysis, employing ANOVA, highlighted statistically significant differences in growth metrics, indicating the critical role of media composition in optimizing growth conditions. Methodologically, the study utilized explants from 2–3-year-old tea plants, which underwent sterilization before being introduced to two distinct culture media for their micropropagation and rooting phases. Statistical analyses were conducted to evaluate the differences in growth outcomes between media, while machine learning models were employed to predict the efficacy of micropropagation and rooting based on various growth regulators. This approach allowed for a comprehensive evaluation of the model's performance in simulating plant growth under different conditions, leveraging metrics like R2, RMSE, and MAE. The findings from this study significantly advance the understanding of tea plant micropropagation, highlighting the utility of machine learning models in agricultural optimization. This research contributes to enhancing micropropagation strategies for the tea plant and exemplifies the transformative potential of integrating machine learning into plant science, paving the way for improved agricultural and horticultural practices. This interdisciplinary approach offers a novel perspective on optimizing in vitro propagation processes, contributing substantially to plant tissue culture and biotechnology. [ABSTRACT FROM AUTHOR]