Plant breeding is the study of creating new plant types with desired characteristics such as amplified yields, resistance, and adaptation to ecological stresses. Modern plant breeding employs computer tools and bioinformatics to facilitate the effective management, analysis, and integration of large-scale genetic and phenotypic data. Developments in genomics, transcriptomics, and technologies provide the computational frameworks within bioinformatics to organize and analyze this data, allowing breeders to look for genetic markers, predict outcomes of traits, and optimize breeding strategies. Hence, key resources include databases for storing and sharing genomics information and breeding management systems that may integrate private and public datasets. These provide insight into genetic diversity, trait inheritance, the environment to support marker-assisted selection, genomic prediction, and many more with the help of computational tools. Advanced algorithm-driven tools enable predictive modelling and support data-driven decisions, substantially increasing breeding strategies’ accuracy. Therefore, this study aims to provide brief knowledge of computational tools and databases in plant breeding research and discuss the methods and advancements in this research, along with other essential information to get complete insight.