Machine learning and artificial intelligence are widely used in many scientific and technological domains, and they have lately been used to enhance several plant tissue culture stages. Technological advancements have transformed the way plant genotypes and phenotypes are measured, resulting in the everyday generation of massive, complicated data sets. Specifically, pertinent to the advancement of innovative food and farming technologies is the creation of verified, significant, and practical methods for merging, contrasting, and displaying extensive, multifaceted data sets from various sources and scientific methodologies. Simultaneously, machine learning has advanced quickly, becoming extensively used in science generally and in plant genotyping and phenotyping specifically. In this article, we examine machine learning's use in the field of plant science. We demonstrate how a variety of techniques provided by machine learning allow scientists to identify significant patterns in pertinent plant data