Smart breeding for better fish for food using genomics and AI

The accelerating demand for aquatic food, coupled with climate change and environmental constraints, is driving the aquaculture sector toward transformative genetic technologies. Smart breeding—defined as the integration of genomics and artificial intelligence (AI) for genetic improvement—has emerged as a promising paradigm for accelerating genetic gain, improving resilience, and reducing environmental impacts. However, despite rapid technological progress, the translation of these tools into routine commercial breeding remains uneven, constrained by biological complexity, data limitations, regulatory uncertainty, and socio-economic barriers. This review synthesizes current progress in genomics- and AI-enabled breeding for aquaculture, critically evaluating genomic resources, quantitative trait analysis, genome editing, machine-learning prediction, automated phenotyping, and decision-support tools for breeding program design. We highlight that genomic selection and digital phenotyping are approaching operational maturity in several species, whereas genome editing and fully AI-optimized breeding pipelines remain experimental. We identify key systemic barriers, including data standardization, economic accessibility, governance frameworks, and workforce development, and outline priorities for the next decade, including multi-omics integration, locally adaptive breeding strategies, sustainability-focused trait selection, and collaborative data infrastructures. Smart breeding, if responsibly implemented, can become a cornerstone of sustainable aquatic food production and global food security.