The integration of artificial intelligence (AI) into livestock genetics and genomic selection represents a transformative shift in modern animal breeding. AI-driven approaches, including machine learning (ML) and deep learning (DL), enhance the accuracy and efficiency of genomic selection (GS), enabling early prediction of breeding values, optimized trait selection and improved disease resistance. This chapter explores the core applications of AI in livestock genetics, such as genomic prediction, image-based phenotyping and precision health monitoring, while highlighting the technological foundations—next-generation sequencing, high-performance computing and IoT-enabled data collection—that make these advancements possible. Case studies across dairy cattle, swine, poultry and aquaculture demonstrate AI’s real-world impact, from accelerating genetic gain to enhancing animal welfare. However, challenges such as data quality, model interpretability and ethical concerns around genetic modification and data ownership must be addressed. Future directions include multi-omics integration, digital twins and quantum computing for advanced breeding optimization. By balancing innovation with ethical responsibility, AI can drive sustainable, equitable progress in livestock production. The goal is to illustrate how AI can be a powerful ally in developing a smarter, more sustainable future for livestock sectors.
Artificial intelligence, Livestock genetics, Genomic selection, Machine learning, Precision breeding, Animal genomics, Bioinformatics, Ethical AI
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