Recent advancements in genome sequencing, bioinformatics, and omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revolutionized molecular studies and crop breeding by enabling unprecedented discoveries in gene functionality, molecular mechanisms, and crop protection strategies. Functional genomics and high-throughput omics approaches have significantly enhanced our understanding of crop genetics, facilitating the development of new and improved crop varieties. However, the complexity of big data and the heterogeneity of omics datasets necessitate advanced analytical tools such as machine learning (ML). Integrating ML with omics approaches has transformed data analysis, enabling efficient processing of massive datasets and accelerating genetic advancements in agricultural breeding. Machine learning algorithms, including supervised, unsupervised, and deep learning techniques, integrate diverse datasets, handle high-dimensional data, and classify biological functions with significant accuracy and efficiency. These methods uncover hidden patterns, predict gene functions, and prioritize candidate genes for experimental validation. This chapter explores recent advances at the intersection of machine learning and crop genomics, focusing on ML algorithms and models for omics data analysis, as well as the application of ML-based tools to address global challenges such as food security and climate resilience while significantly accelerating crop breeding in the future.