Although significant efforts have been made to increase the world's food supply, hunger and undernutrition continue to be significant challenges worldwide. As the global population is expected to exceed 10 billion by 2050 and agriculture facing numerous challenges due to climate change, finding sustainable solutions to food insecurity is crucial. Plant tissue culture has emerged as a promising technology for crop improvement and rapid multiplication of various crops. However, this approach generates vast amounts of data resulting from the complex interactions between plant genetic and environmental components, making it difficult to analyze using traditional statistical methods. To tackle this issue, researchers have turned to artificial intelligence (AI) technologies, particularly machine learning (ML) algorithms, which are well-suited for handling large and complex datasets. Artificial neural networks, support vector machines, genetic algorithms, and other ML techniques have been extensively employed in the analysis, prediction, and optimization of plant in vitro breeding processes. Thus, this mini-review provides an up-to-date assessment of machine learning applications in plant in vitro culture research, emphasizing their various strengths and limitations and proposing potential future research directions.