Machine learning is a powerful tool for analyzing large and complex genetic and genomic data. This review outlines key applications of machine learning in genomics, including the identification of genomic elements such as transcription start sites (TSSs), splice sites, promoters, and enhancers. Machine learning can also be used to predict gene expression, assign functional annotations to genes, and understand the mechanisms underlying gene expression. The review discusses the main categories of machine learning methods—supervised, unsupervised, and semi-supervised—and the challenges associated with their application in genomics. It emphasizes the importance of choosing the appropriate method based on the data and the problem at hand. Supervised learning requires labeled data, while unsupervised learning does not, and semi-supervised learning uses a combination of labeled and unlabeled data. The review also addresses the trade-offs between predictive accuracy and interpretability, and the importance of incorporating prior knowledge into machine learning models. It highlights the role of feature selection in improving model performance and the challenges of handling imbalanced data and missing values. The review concludes by discussing the future of machine learning in genomics, emphasizing its potential to advance our understanding of complex biological systems.Machine learning is a powerful tool for analyzing large and complex genetic and genomic data. This review outlines key applications of machine learning in genomics, including the identification of genomic elements such as transcription start sites (TSSs), splice sites, promoters, and enhancers. Machine learning can also be used to predict gene expression, assign functional annotations to genes, and understand the mechanisms underlying gene expression. The review discusses the main categories of machine learning methods—supervised, unsupervised, and semi-supervised—and the challenges associated with their application in genomics. It emphasizes the importance of choosing the appropriate method based on the data and the problem at hand. Supervised learning requires labeled data, while unsupervised learning does not, and semi-supervised learning uses a combination of labeled and unlabeled data. The review also addresses the trade-offs between predictive accuracy and interpretability, and the importance of incorporating prior knowledge into machine learning models. It highlights the role of feature selection in improving model performance and the challenges of handling imbalanced data and missing values. The review concludes by discussing the future of machine learning in genomics, emphasizing its potential to advance our understanding of complex biological systems.