Micropropagation techniques provide an opportunity to induce in vitro organogenesis and proliferation, enabling the maintenance and comprehension of the molecular mechanisms of plant responses in precisely controlled conditions, free from external factors. This forms the foundation for numerous applications in plant biotechnology and breeding new crop varieties. Unfortunately, current strategies to optimize in vitro organogenesis are cumbersome and time-consuming because of the enormous variety of parameters that influence plant cell and tissue culture. Therefore, there is a significant potential to employ novel computational approaches for data analysis in order to produce more efficient protocols. Artificial intelligence (AI) and machine learning (ML) are widely used in many scientific and technological domains, and they are currently employed to enhance various stages of micropropagation. ML technology stands out as an innovative approach to characterize the combined role of different plant growth regulators, classify somatic embryos, predict and optimize the length and number of in vitro raised shoots or roots, and optimize environmental conditions to maximize productivity. Despite its potential, due to complex computational algorithms, the implementation of AI and ML in this field has yet to achieve its maximum potential. Therefore, this chapter concentrates on the current advancements in plant tissue culture using AI–ML. A number of modelling principles, as well as different algorithms, have been discussed and highlighted. This chapter also offers an innovative viewpoint for future study objectives.