The prolonged growth cycle of Phalaenopsis orchids during in vitro propagation negatively impacts economic productivity by increasing both the cost and time required to harvest the product, thereby posing challenges for producers. This study aimed to accelerate the in vitro growth of Phalaenopsis plantlets by optimizing the concentration of macronutrients in the medium and to evaluate the impact of these modifications on the efficiency of photosystem II using artificial neural networks (ANNs) as a machine learning approach. the efficiency of photosystem II, as a key indicator of plant health, is directly related to increased in vitro growth and quality of plantlets. Various macronutrient concentrations were tested, including four levels of ammonium and nitrate, three levels of potassium, and five levels of phosphorus, relative to a basal culture medium used as the control. A total of 120 experimental data points were obtained for ANN-based modeling, by implementing an iterative prediction scheme, determined the optimal levels of macronutrients to increase the efficiency of photosystem II in plants. The performance of the model was evaluated by the coefficient of determination and mean squared error of 0.94 and 0.007, respectively. The results demonstrated that increased potassium concentration, particularly in conjunction with changes in nitrate levels, plays a crucial role, while the combined elevation of ammonium and phosphorus concentrations significantly improves photosystem II performance. Machine learning-based predictions identified the optimal culture medium as containing a twofold increase in nitrate and a 1.89% increase in potassium concentration, leading to a 7% improvement in photosystem II efficiency.