Agricultural sustainability and production are greatly affected by the quality of its seeds. Chemical analysis and hand examination are two common examples of traditional methods of assessing the quality of seeds that are time-consuming, laborious, and prone to human error. The evaluation of seed quality can now be improved and automated with the help of machine learning (ML) techniques, which have become more prevalent recently. This book chapter offers a thorough summary of how machine learning is used to assess different seed quality measures, including vigor, disease detection, and germination potential. At the outset of the chapter, the basic ideas of seed quality and the shortcomings of traditional assessment methods are presented. After then, it looks at how the ML algorithms deep learning model, unsupervised learning model, and supervised learning model, can be applied to gather essential information from environmental data, phenotypic characteristics, and seed images. The chapter also covers the methods for training and validating these models with labelled datasets in order to predict seed quality with high efficiency and accuracy. The use of deep learning frameworks to evaluate the good health of seeds, the prediction of germination rates based on environmental factors, and the image-based classification of seed defects are some of the primary aspects explored. In order to facilitate real-time seed quality monitoring, this chapter also emphasizes the integration of machine learning (ML) models with Internet of Things (IoT) devices and remote sensing technologies. There is also discussion of issues such as the lack of data, the interpretability of models, and the requirement for domain-specific knowledge in order to create reliable machine learning solutions. In order to improve seed quality estimate and enable sustainable agriculture, the chapter ends with future research topics and possible breakthroughs in incorporating machine learning for precision agriculture.