Enhancing Genomic Selection in Dairy Cattle Through Artificial Intelligence: Integrating Advanced Phenotyping and Predictive Models to Advance Health, Climate Resilience, and Sustainability

Abstract

The convergence of genomic selection and artificial intelligence (AI) is redefining precision breeding in dairy cattle, enabling earlier, more accurate, and multi-trait selection for health, fertility, climate resilience, and economic efficiency. This review critically examines how advanced genomic tools—such as genome-wide association studies (GWAS), genomic breeding values (GEBVs), machine learning (ML), and deep learning (DL) models to accelerate genetic gain for complex, low heritability traits. Key applications include improved resistance to mastitis and metabolic diseases, enhanced thermotolerance, reduced enteric methane emissions, and increased milk yield. We discuss emerging computational frameworks that combine sensor-derived phenotypes, omics datasets, and environmental data to support data-driven selection decisions. Furthermore, we address implementation challenges related to data integration, model interpretability, ethical considerations, and access in low-resource settings. By synthesizing interdisciplinary advances, this review provides a roadmap for developing AI-augmented genomic selection pipelines that support sustainable, climate-smart, and economically viable dairy systems.

1. Introduction

Genetic selection procedures have revolutionized the global dairy industry. Contemporary dairy cows yield over twice the milk produced by their counterparts from 50 years ago, with more than half of this enhancement attributable to genetic selection. The fundamental components for genetic enhancement have been performance records and pedigree data. The establishment and extensive use of national milk recording systems, the implementation of artificial insemination, and the advancement of precise genetic evaluation techniques have facilitated significant genetic improvement in dairy cattle herds [1]. Breeding indices serve as essential instruments in contemporary dairy cattle breeding. They offer a method to combine multiple trait-related datasets into a singular metric that may be utilized for ranking animals and informing breeding choices [2]. The benefits of the global dairy industry have been directly impacted by the significant changes that the technology has brought about in countries with dairy traditions [3]. The identification and mapping of the bovine genome represent one of the most significant advancements in the evolution of instruments employed for the genetic enhancement of cattle in recent decades. The application of high-performance genomics in dairy cattle selection signifies a significant advancement in enhancing biological and genetic progress across many traits and indices in multiple animal production systems [4]. This advancement has the potential to revolutionize the efficiency and productivity of dairy cattle breeding programs. Genomic selection (GS) pertains to selection decisions informed by genomic breeding values (GEBV) [5]. GS is undoubtedly the most promising method for enhancing genetic gain in domestic animals to have emerged in recent decades; however, it is a costly process [6].

The results of genetic enhancement are permanent and cumulative, rendering it a potent instrument for enhancing the sustainability of animal agriculture. In contrast to nutritional and animal health interventions, which require continuous inputs, genetic enhancements that are implemented in one generation are heritable and persist across generations. Additionally, genetic solutions for animal health and welfare issues frequently demand fewer labor and material inputs than chemical or mechanical methods [7]. The escalating global need for food, driven by the persistent rise in the human population, underscores the urgent need to enhance efficiency and sustainability of animal production systems. GS in dairy cattle is mostly utilized in the five predominant breeds: Holstein, Jersey, Brown Swiss, Guernsey, and Ayrshire, and has not been extensively implemented in crossbred populations that necessitate intricate genomic models [8].

Disease epidemics have far-reaching consequences for environmental sustainability, public health, and animal welfare, in addition to resulting in direct economic losses. GS can be instrumental in addressing these challenges. Genomic technologies present a promising opportunity to enhance the health and productivity of cattle by enhancing their disease resistance, adaptability to changes in production systems, resilience to stress, and social behavior [9].

In recent years, the increasing complexity and dimensionality of genomic and phenotypic data in dairy cattle breeding has led to the integration of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), into genetic evaluation pipelines [10]. Genomics, propelled by the emergence of sequencing technologies, has evolved into a domain where researchers engage with vast, heterogeneous, redundant, and intricate omics datasets [11]. These computational approaches have demonstrated superior performance in identifying non-linear patterns, improving prediction accuracy of genomic breeding values, and enhancing multi-trait selection in real-world breeding environments [11]. The synergy between genomics and AI is opening novel opportunities for precision breeding strategies tailored to climate, health, and productivity challenges. Recent advances suggest that DL methods, such as multilayer perceptrons and convolutional neural networks, can slightly improve genomic prediction accuracy for complex behavioral traits in dairy cattle compared to traditional models [12]. However, differences in selection outcomes between DL and conventional approaches may affect long-term genetic gain. High computational demands and limited applicability to non-genotyped animals remain key challenges, highlighting the need for further validation before routine implementation in breeding programs [10]. In practice, a possible advantage of DL algorithms is their capacity to anticipate the comprehensive genetic value of an individual, encompassing dominance, epistasis, and all interactions regardless of their source [13]. Incorporating AI-based approaches into GS represents a paradigm shift, enabling multi-layer data integration and non-linear trait prediction that traditional statistical models cannot achieve.

The objective of this review is to examine how AI can be effectively integrated into GS pipelines in dairy cattle, with particular emphasis on AI’s dual role in (1) generating advanced, high-quality phenotypes through sensor technologies, imaging, and multi-omics data, and (2) applying predictive modeling techniques to improve genomic breeding value estimation, multi-trait selection, and decision-making. By focusing on this integration, the review highlights how AI-enabled phenotyping and genomic prediction can work together to accelerate genetic gain, improve animal health and welfare, enhance climate resilience, and support the long-term sustainability of dairy production systems.

In doing so, this paper also addresses the identification and dissemination of traits associated with disease resistance, fertility, heat stress tolerance, methane emission reduction, and production efficiency, while presenting emerging computational frameworks and practical advancements that enable the transition toward climate-smart and economically optimized dairy systems. Particular attention is given to the application of ML and DL algorithms in predicting GEBVs, improving selection accuracy, and uncovering novel trait associations previously inaccessible via conventional models. By synthesizing recent progress in genomic technologies and AI applications, this review aims to inform researchers, breeders, and industry stakeholders about the next generation of precision selection tools, while emphasizing the biological, technological, and economic potential of AI-augmented genomic evaluation in guiding sustainable, resilient, and welfare-oriented dairy cattle breeding programs.

2. Influence of Genomic Selection on Dairy Cattle Health

The health of cows strongly influences the profitability of dairy farms. Health events result in significant economic losses, encompassing losses from on-farm mortality, premature culling, diminished milk yield, and heightened veterinarian and treatment expenses [1]. Over the past decade, genomics has garnered significant attention in the examination of features associated with animal health and welfare. Consequently, several traits, including resistance to viral and non-infectious diseases and environmental adaptability, have been incorporated as novel selection indices in dairy cattle [4]. Additionally, the utilization of genomic and phenotypic selection is crucial for the enhancement of animal health and welfare, the reduction in antimicrobial product use, the expansion of the milk supply for human consumption, and the establishment of efficient dairy cattle production systems [14]. Genetic selection represents a promising tool for improving health related traits in dairy cattle [15].

2.1. Impact on Disease Resistance and Prevalence

The response of animals to disease challenges has long been a significant interest and economic concern for farmers, likely since the advent of domestication [16]. GS can be employed to estimate the genetic risk of specific health events in dairy cows, such as genetic predictions for retained fetal membranes, ketosis, true gastric displacement, mastitis, and lameness [8,17]. ML algorithms can further enhance the accuracy of genomic selection by uncovering complex, non-linear relationships between health traits and genomic markers [10]. The herd enhancement program has experienced a substantial increase in productivity and health characteristics because of the integration of genomics. The rate of genetic progress in milk production traits has increased by 50–100%. The genetic evaluation process of dairy cows is expedited by the information obtained from genome analysis, which is crucial for animal science and the development of novel genome editing applications [8]. Recent studies have identified several genes associated with immune function and production traits in dairy cattle. For instance, Gutiérrez-Reinoso et al. noted that the CHL1 gene is up-regulated in response to stress and affects the function of the immune system [14]. Nevertheless, other authors have reported that the CFAP69, STEAP2, and ITGB3BP genes, which are situated on chromosome 4, are associated with the immune system mechanisms of the mammary gland [18,19]. Selection strategies aimed at reducing the prevalence of cows with deep udders, specifically low rear udders, widely placed teats, teats positioned posteriorly, and short, wide teats can augment initiatives to decrease mastitis incidence, alongside enhanced health management, therapeutic interventions, and appropriate milking practices [20]. The correlation between genomic, genotypic, and phenotypic udder characteristics is essential for comprehending the health, productivity, and longevity of dairy cattle. The dairy sector faces substantial economic burdens from the elevated incidence and prevalence of both clinical and subclinical mastitis, encompassing treatment expenses, output losses, and diminished animal welfare. For example, GS for clinical mastitis incidence now routinely incorporates genomic breeding values derived from large reference populations, improving selection accuracy and enabling earlier identification of resistant animals. AI-based feature selection methods can prioritize genomic regions most strongly associated with mastitis resistance, such as those related to somatic cell count (SCC), udder depth, and immune function [14,21]. AI-based genomic models enable simultaneous analysis of multi-source data—including genotypes, phenotypes, and sensor-derived health indicators—allowing more precise identification of animals genetically predisposed to mastitis resistance [22]. Future udder health challenges may be mitigated through the selection of animals with lower SCC, a key indicator of mastitis resistance and overall udder health. The accuracy and efficiency of selecting dairy cattle with enhanced mastitis resistance are anticipated to be improved by the implementation of new genomic evaluation models in phenotypic-related studies [14].

While genomic approaches to disease prevention offer numerous advantages, they also present certain limitations that must be carefully considered. Multiple genes influence many cattle diseases, making it challenging to develop genetic markers for effective prediction. Environmental factors, management techniques, and pathogen heterogeneity also affect disease outbreaks, confounding genetics and disease resistance. Due to disease interactions, selecting for resistance to one illness may enhance vulnerability to others. The integration of genomic data into breeding programs requires advanced bioinformatics tools and a comprehensive understanding of herd-level genetic diversity. Breeders, veterinarians, and researchers must work together and monitor illness occurrence and genetic performance to adopt GS for disease resistance [9,23].

Traits such as fertility and health often exhibit lower heritability estimates (<5%) compared to production traits (>30%); nevertheless, adequate genetic diversity is present to facilitate selection for traits with low heritability [17]. An overview of the heritability estimates for a range of health-related traits in dairy cattle is presented in

Table 1. Heritability estimates for genetic resistance to common diseases in dairy cattle.

Genetic Resistance to Disease	Range in Heritability Estimate	Reference
Respiratory disease in pre-weaning calves	0.11	[24]
Respiratory disease in post-weaning calves	0.07	[24]
Bovine respiratory disease	0.07 and 0.29	[25]
Mastitis in Irish Holstein-Friesian dairy cows	0.05	[26]
Lameness in Irish Holstein-Friesian dairy cattle	0.04	[26]
Lameness	0.00 to 0.02	[17]
Metabolic disorders	0.00 to 0.06	[17]
Johne’s disease	0.05 to 0.15	[27,28]
Displaced abomasum	0.15 to 0.31	[29,30]
Hypomagnesaemia	0.004	[31]
Ketosis	0.01 to 0.16	[29,30]
Hypocalcaemia	0.01 to 0.13	[32,33]
Retained placenta	0.02	[34]
Metritis	0.01	[34]
Cystic ovaries	0.02	[34]
Concentrations of plasma β-hydroxybutyrate	0.17	[35]
Concentrations of milk β-hydroxybutyrate	0.16	[35]
Concentrations of milk acetone	0.10	[35]
Milk fever	0.07–0.11	[36]
Mastitis	0.01 to 0.03	[17]

, illustrating the genetic potential for selection despite low heritability values in many disease resistance traits.

Recent advances demonstrate that AI-enhanced phenotyping—such as automated gait scoring, mastitis detection via thermal imaging, and immune biomarker profiling—can generate high-quality, real-time health indicators [37]. When integrated with genomic Single nucleotide polymorphism (SNP) data in ML models (e.g., random forest, gradient boosting), these phenotypes significantly improve the accuracy of GEBV estimation for disease resistance traits [38]. Such combined pipelines enable breeders to rank animals not only on genetic potential but also on predicted resilience under diverse farm conditions, thereby accelerating genetic gain in udder health, lameness resistance, and metabolic disease tolerance [39]. AI-based models, specifically a combination of least absolute shrinkage and selection operator (LASSO) logistic regression and DL, can effectively classify cows as susceptible or resistant to mastitis using whole-genome sequence data. The optimal model achieved an area under the receiver operating characteristic curve (AUC) of 0.75 and identified key SNPs linked to genes involved in immune response and protein synthesis. This approach demonstrates that AI-enhanced feature selection can improve genomic prediction accuracy for health traits like mastitis, even in high-dimensional genomic datasets [40].

By identifying genetic markers that are linked to resistance to these diseases, it will be possible to select animals that are inherently less susceptible, thereby reducing the necessity for antibiotics and minimizing the risk of antimicrobial resistance. Producers will have enhanced access to powerful tools that enable them to make data-driven decisions, thereby safeguarding animal health and welfare and enhancing the sustainability of cattle production as genomic technologies become more integrated into herd management practices. The continuous monitoring and evaluation of both genetic performance and disease occurrence plays a pivotal role in advancing and optimizing GS methodologies over time. Ultimately, the successful integration of genomic data into breeding plans has the potential to greatly reduce the impact of diseases on livestock populations and improve overall animal welfare.

2.2. Adaptation in Heat-Stressed Dairy Cattle

Certain breeds have reduced susceptibility to heat stress, including smaller, lighter-colored animals, or breeds that demonstrate significant physical and physiological adaptability to heat stress. If heat tolerance is a heritable trait, selective breeding for heat tolerance may enhance animal adaption to climatic stress [41]. Heat stress (HS) in cattle can be alleviated through the provision of shade, fans, and sprinklers [42]. Genetic variation in thermoregulation during heat stress is present within species, including cattle breeds. The literature suggests that tropical breeds, such as Zebu (Bos indicus), exhibit superior tolerance to temperature and humidity compared to temperate breeds (e.g., Holsteins), partly due to the lower productivity of Zebu cattle [43]. Identifying dairy cows with greater heat stress tolerance will be a crucial technique for choosing and managing dairy herds that are more resilient to future climatic changes [44].

Collier et al. [45] divided the genes involved in the heat stress response in cattle into three groups: genes that affect hair and coat traits, genes that affect the cellular response, and genes that affect the whole body [45]. In the first group, Olson et al. [46] found a key gene that is passed down dominantly and controls the “slick hair” phenotype. This phenotype is linked to heat stress tolerance by keeping rectal temperatures low [46]. A significant number of genes have been identified as associated with HS responses by genomic and transcriptome investigations across many animals. Heat shock transcription factors (HSF) and the heat shock protein genes (HSP) that go with them are the main genes involved in how cells react to heat stress [47]. Sonna et al. [48] identified at least 50 genes, distinct from the HSF or HSP families, exhibiting differential expression under heat stress conditions [48]. Collier et al. [45] summarize the gene groups involved in the heat stress response in cattle by looking at changes in expression. They focus on genes that are linked to increased oxidation of glucose and amino acids, decreased oxidation of fatty acids, activation of the endocrine stress response, and activation of the immune system through extracellular secretion of heat shock proteins [45]. On top of the evidence from transcriptome studies, genomic studies have shown the existence of genes linked to hair and skin traits, immune system response, nervous system function, and resistance to ticks [47]. According to J. B. Garner et al. [49] dairy cows that are genetically more likely to be heat tolerant have less milk production loss and lower core body temperature increases during a simulated heat wave than cows that are genetically more likely to be heat susceptible. Therefore, in a time when heat stress events are occurring more frequently and lasting longer, genetic selection for heat tolerance may increase the resilience and welfare of dairy herds globally as well as the productivity of dairy farming [49].

Genetic factors have been identified as a significant factor in the variability of dairy cows’ HS responses [50]. For instance, Otto et al. [51] conducted genome-wide association studies (GWAS) in the Gir × Holstein F2 experimental population to identify genetic markers responsible for genetic variation in response to HS [51]. They employed the breed of origin of alleles approach to assess the origin of marker alleles of candidate genes. The authors discovered that most animals that responded more favorably to the effects of HS possessed two alleles from the Holstein breed, whereas a significant number of heat-stressed animals possessed two alleles from the Gir breed. This suggests that the alleles of the Holstein breed may be associated with a more intricate response to the effects of HS. This could be attributed to the fact that Holstein animals are more susceptible to HS than Gir animals, resulting in the development of more intricate genetic mechanisms to protect the body from the detrimental effects of HS. Consequently, the genetic variation in the trait can be better understood by revealing the origin of marker alleles of candidate genes for heat tolerance in dairy cattle populations. This information is subsequently used to estimate breed-specific SNP effects, thereby improving genomic prediction for heat tolerance and production traits in dairy cattle [50]. The identification of molecular markers or SNPs linked to heat stress tolerance has been suggested as a crucial technique to maintain productivity in cattle subjected to thermal stress [52]. Consequently, investigating the genetic foundations linked to thermotolerance may facilitate the selection of cattle suited for warm semiarid environments [53]. Although few causative genes have been identified, SNP markers associated with thermal tolerance have been detected in dairy cattle. Mutations in the prolactin receptor gene (PRLR) have a significant impact on thermal tolerance. This variant results in the SLICK phenotype (Slick Hair gene), which is readily identifiable by the short, sleek coat that it bestows upon animals. In comparison to wild-type Holsteins, Holstein cattle that were introduced with the SLICK haplotype exhibited enhanced thermoregulatory capabilities [54]. Holstein cows possessing the slick hair gene demonstrated superior thermoregulatory capabilities compared to non-slick counterparts and exhibited less severe declines in milk production during the summer months. These results were attributed to their superior capacity to evaporate perspiration at the epidermal surface [55].

Genome-wide association studies are widely used to scan tens of thousands of genomic variants to identify those statistically associated with heat tolerance traits in cattle. While many associations have been detected, biological validation is essential to confirm causality [50]. Among the most consistently validated genomic markers for thermotolerance in dairy cattle are quantitative trait loci (QTL) on BTA3, 6, 14, and 17, which have been repeatedly associated with indicators such as rumination time, rectal temperature, and milk yield under heat stress [51,56,57,58,59]. Notably, BTA14 has emerged as a key region, with multiple studies linking it to both heat tolerance and production traits, suggesting a dual role in maintaining productivity under thermal stress [50,60,61]. The PRLR gene carrying the SLICK haplotype is well established as a functional mutation that enhances thermoregulation [54,55]. Additional QTLs on BTA4, 8, 12, 13, 15, 21, 24, and 29 [57,58,59,62], along with candidate genes such as AMFR, ADGRB1, DENND3, DUSP16, EFR3A, EPS8, FASN, MGP, PIK3C2G, SMARCE1, CCDC57, and PAEP [62,63], show promise but require further validation across breeds, climates, and management systems. These loci are potentially linked to thermotolerance through physiological pathways including lipid metabolism, immune function, and stress signaling, though their effects have not yet been consistently replicated across independent datasets. Continued integration of GWAS, functional validation, and AI-driven genomic prediction will be essential to distinguish robust biomarkers from context-specific associations.

Genetic factors play a critical role in enabling dairy cattle to maintain milk output under high-temperature conditions [64]. The identification of reliable biomarkers opens new avenues for selective breeding programs aimed at improving heat tolerance [65]. Moreover, integrating real-time phenotypic data—such as body temperature, respiration rate, and rumination time—from precision sensors with genomic markers through AI models (e.g., deep neural networks, ensemble methods) can significantly enhance the accuracy of GEBV’s for thermotolerance. This combined GS–AI approach supports the selection of animals that not only carry confirmed heat-tolerant alleles but also demonstrate superior adaptive capacity in real-world production environments.

2.3. Harnessing Genetics to Reduce Enteric Methane Emissions in Livestock

Climate change has presented challenges to cattle production, such as the necessity of adapting to changing climates and the reduction in greenhouse gas (GHG) emissions [66]. Enteric methane is a major greenhouse gas emitted by livestock production systems and is an important contributor to global warming [67]. Methane (CH₄) is a natural byproduct of ruminant digestion, generated by the rumen microbiota through the metabolism of carbon dioxide (CO₂) and hydrogen (H₂), and it is one of the most worrisome greenhouse gases [68]. CH₄ possesses a global warming potential 28 times greater than that of CO₂ [69]. CH₄ emissions from dairy animals alone are responsible for 18% of global greenhouse gas emissions [67]. The current obstacle to the environmentally sustainable production of cattle is the mitigation of methane emissions from the livestock sector [67]. A potential technique is employing genetic selection to diminish CH₄ production [69]. A genetic factor for methane emissions has been demonstrated [70]. While methane emissions are typically classified as an environmental and efficiency-related trait, they also have indirect links to animal health [66]. Methane emissions are closely associated with rumen fermentation patterns, feed conversion efficiency, and energy metabolism—processes that directly affect body condition, immune competence, and reproductive performance [71]. Excessive methane production may indicate suboptimal fermentation efficiency, which can be linked to digestive disturbances or imbalances in the rumen microbiota. Therefore, reducing methane emissions through genetic selection could, in some cases, support better nutrient utilization and overall health, if selection indices are designed to maintain or improve productive and physiological traits [72]. Moreover, a genetic factor in methane emissions has been established. Adaptation to adverse climatic circumstances must be accomplished by enhancing efficiency, leading to reduced GHG emissions and underscoring the significant interrelation between the bovine pastoral system and climate [73]. Methane emissions are a heritable trait, with estimates ranging from 0.12 to 0.52, and they could be reduced through selection [8]. Dairy cow methane emissions can also be reduced through improved feeding and ration design. Improved feeding and ration design represent a scientifically validated and economically viable strategy for mitigating enteric methane emissions in dairy cows [74]. Future research should integrate methane traits into genomic selection frameworks to enable cumulative genetic progress and develop economic models that support farm-level adoption. GS may lead to a decrease in CH₄ emissions, as genetic variability affects ruminant CH₄ yield. By decreasing enteric CH₄ emissions, GS would facilitate environmental adaptation and increased productivity in developing countries. The most feasible method of reducing CH₄ emissions and accomplishing permanent reductions is through the selection and breeding of low methane emitting farm animals through GS. Training populations that possess genotypic data and measurements for CH₄ were necessary for the successful implementation of GS for CH₄. GS has the potential to identify significant genomic regions, candidate genes, biomarkers, and rumen microbial genes that may be associated with the reduction of CH₄. A cost-effective method of reducing CH₄ is to enhance the genetic composition of the livestock [8]. Pickering et al. [75] identified a heritability of 0.05 (SE 0.07) for CH₄ production (mg/kg), measured using a laser methane detector; nevertheless, this estimate lacked statistical significance. Lassen and Løvendahl [76] determined a heritability of 0.21 (SE 0.06) for CH₄ production (g/d), assessed via Fourier-transform infrared spectroscopy during milking. Pszczola et al. [77] reported an average heritability of 0.27 (average SE 0.09) for CH₄ output (g/d), assessed using the same methodology [77]. These studies provide initial evidence that methane output in dairy cattle is heritable. Nonetheless, the heredity of CH₄ production remains inadequately defined, and the associations between CH₄ production and associated variables, such as milk yield and body weight, are currently unknown [69]. The research by I.S. Breider et al. [69] established that CH₄ production exhibits moderate heritability, indicating the feasibility of genetically selecting cows for reduced CH₄ emissions [69]. CH₄ exhibits a genetic correlation ranging from +0.79 to +0.86 with weaning weight, yearling weight, ultimate weight at 20 months of age, and dry matter intake (DMI) in Bos taurus beef cattle, as reported by Donoghue et al. [78]. While genetic selection for reduced enteric methane emissions may be associated with lower feed intake, it does not inherently necessitate a decline in animal productivity. The use of multi-trait selection indices enables the simultaneous improvement of both environmental and production traits, allowing for the development of cattle that are both efficient and climate resilient [79]. An alternative is to choose based on residual methane characteristics, which have negligible genetic correlation (−0.04 and −0.05) with DMI, as noted by Donoghue et al. [78]. The potential and limitations of the utility of integrating these data are still in the early stages of understanding.

Further research is needed to fully understand the genetic factors influencing CH₄ production in dairy cattle and how it relates to other important production traits. By identifying and selecting for cows with lower methane emissions, the dairy industry could potentially reduce its environmental impact and contribute to sustainable farming practices. With continued study and advancements in genetic selection, it may be possible to breed dairy cattle that are not only more productive but also more environmentally friendly.

3. Enhanced Productivity

Dairy cow selection programs seek to enhance the profitability and sustainability of the dairy sector by focusing on features that either augment revenues or diminish costs. Breeding aims are generally approached through selection indices, where pertinent features are aggregated and weighted based on their economic significance [1].

3.1. Milk Yield

The average yearly milk production has risen from approximately 13,000 to 28,000 pounds during the past 60 years. A significant portion of this enhancement in productivity can be attributed to genetic selection [1]. Studies have demonstrated that GS enables breeders to identify cattle with superior genetic potential for increased milk production, improved fat and protein composition, and overall udder health [80]. Additionally, ML models incorporating genomic breeding values have been successfully used to predict daily milk yield, further optimizing dairy farm efficiency [81]. The ability to identify high-yielding animals early reduces the generation interval, meaning that the time between generations is shortened. This efficiency translates to quicker improvements in milk production across the herd [8].

Throughout the last century, the primary objectives of dairy cattle breeding programs have been to improve milk production and composition, resulting in a significant rise in milk yield [82]. Clear disparities in productivity across Europe are revealed by the regionalized analysis of milk yield trends from 2004 to 2023 (Figure 1). Denmark and Estonia, two countries in Northern Europe, exhibit the highest yields, with both surpassing 10,000 kg/cow/year by 2023. Estonia has demonstrated exceptional development over the past two decades. The Netherlands and Germany sustain high productivity, while France and Austria demonstrate moderate but consistent increases. Western Europe’s performance is consistent and robust. Even though Spain and Italy have made significant strides, Southern Europe continues to lag behind the northern and western regions. Cyprus, Greece, and Malta continue to have yields that are comparatively lower. The most significant variation is observed in Eastern Europe, where Czechia, Slovakia, and Lithuania have made significant progress, while Romania and Bulgaria have made minimal progress, suggesting that there are persistent structural and technological limitations. Genomic selection has significantly increased the rate of genetic improvement in milk production traits. Importantly, part of the observed increases in milk yield, particularly in Northern and Western Europe, can be attributed to the implementation of genomic selection programs starting in the late 2000s, which have accelerated the rate of genetic progress in milk production traits. However, these gains are also influenced by improvements in nutrition, precision feeding, herd management, and broader technological adoption. In general, the graph underscores the significance of targeted investment in genetics—including GS—alongside technology and management in lower-yielding regions. Without such investments, the existing productivity divide is likely to widen, as countries with higher yields continue to advance at a faster pace.

Advances in genetic selection, especially the use of breeding indices that give priority to milk production traits, are primarily responsible for the notable increases in milk yield over the past few decades. As a result, generation intervals have shortened, and genetic advancement has accelerated. Crucially, research on milk yield has revealed moderate to high heritability estimates, usually between 0.20 and 0.55, suggesting that selective breeding is still a potent technique for increasing productivity [82]. GS has transformed breeding strategies by enabling earlier and more accurate identification of high-yielding animals, reducing generation intervals, and accelerating genetic gain [84]. When combined with AI, particularly through the integration of automated milking system (AMS) data and sensor-derived phenotypes, GS provides a powerful framework for selecting animals that combine high milk yield with resilience, longevity, and improved health—all of which are essential for the long-term sustainability of the dairy industry.

Genetic selection for higher milk yield has driven dairy intensification, resulting in specialized production systems, larger herds, and reliance on grains and protein sources. Despite productivity gains, the dairy industry’s long-term success depends on more sustainable breeding and management goals, notably agroecological ones. Current high-producing systems must be improved to prioritize animal health and wellbeing, environmental efficiency, climatic adaptation, and genetic diversity to prepare for future difficulties. Breeding programs now include several of these features in their goals, but much work remains [85].

Integrating continuous AMS-derived phenotypes—such as milk flow patterns, yield stability, and recovery rates after health events—with genomic SNP data through AI-based predictive models can refine GEBV estimation for milk yield and persistency [86]. This approach allows selection for both high production potential and resilience, thereby enhancing long-term herd profitability.

Breeding programs that incorporate genomic indices can significantly improve milk yield by selecting animals with superior genetic traits [87]. Proper feeding and nutrition are essential for maximizing milk production. Nutritional modifications can quickly lead to increases in milk yield, particularly when diets are optimized for energy balance, protein content, and micronutrient availability [88]. A study in Germany with 34,497 German Holstein cows published in 2024, explored the genomic connection between milk yield and health traits in German Holstein cows. It highlighted the importance of incorporating functional and evolutionary information in genomic analyses to improve the understanding of biological mechanisms and the accuracy of genomic predictions [89]. Advances in GS have enabled breeders to identify and propagate cattle with superior genetic potential, leading to enhanced productivity and sustainability [7]. Studies have shown that genes associated with milk protein synthesis, such as CSN1S1, CSN2, and LALBA, significantly impact milk quality and yield [90]. Additionally, variations in genes related to metabolic efficiency, such as DGAT1 and ABCG2, contribute to higher fat and protein percentages in milk, improving its economic value [91]. The integration of genomic breeding values (GBV) has also been linked to improved resilience against metabolic diseases, reducing production losses due to health issues [92].

By leveraging genomic indices, dairy farmers can enhance genetic selection strategies, resulting in increased milk output, better feed conversion efficiency, and overall improved herd performance. Numerous studies worldwide have demonstrated that the application of genomic indices significantly enhances dairy cattle productivity. GS involves using deoxyribonucleic acid (DNA) markers to predict the genetic merit of animals, thereby facilitating more accurate and efficient breeding decisions. The study investigates the genetic determinants of key milk production traits, including 100-day milk yield, 305-day milk yield, total milk yield, and persistency in first calving Holstein cattle in Türkiye. Using a GWAS approach, the research identifies significant SNP associated with these traits 1 and 2. The study highlights the substantial genetic component underlying these economically important traits, with moderate to high heritability (0.32–0.54) suggesting the feasibility of targeted genetic improvement strategies [93].

The integration of GS and management protocols is expected to enhance the profitability of dairy production by improving milk yield, cow health, longevity, and fertility [94]. By providing a large reference population for high milk production, GS facilitates the identification and propagation of desirable traits within dairy herds [8].

It is important for the dairy industry to shift towards more sustainable practices to ensure its viability in the long run. This includes prioritizing animal welfare, environmental impact, and genetic diversity to adapt to changing conditions. While production traits continue to serve as foundational components of genetic selection programs, there is a discernible shift toward prioritizing characteristics that support animal welfare, reproductive efficiency, and environmental sustainability. Future developments will integrate whole-genome sequencing and multi-omics data to refine selection for complex traits like feed efficiency and milk composition [4].

3.2. Fertility

Reproductive efficiency is a critical economic characteristic in dairy cattle [8]. Reproductive inefficiency leads to prolonged calving intervals, elevated involuntary culling rates, diminished milk output, and postponed genetic advancement, among other issues, resulting in substantial economic losses. Enhancing bovine fertility through genetic selection is a global priority. It is well-established that production and fertility are inversely connected, and selection programs that have prioritized yield while disregarding fertility have ultimately witnessed a fall in reproductive performance [1]. The integration of female GS and reproductive technologies has augmented annual monetary genetic gain in dairy breeding projects [95]. The heritability of reproductive qualities is minimal, however they can be enhanced via direct genetic selection [96]. Notwithstanding the progress in hormone synchronization techniques, outcomes remain inconsistent due to genetic, physiological, and metabolic disparities among cows [97]. To effectively disseminate elite genomes, reproductive technologies such as AI and embryo transfer have been employed to accomplish genetic improvements in numerous species through within-breed selection. Information regarding the productive and reproductive performance attributes of the population is necessary for any genetic improvement activity in dairy cattle [98].

In industrialized nations, livestock breeding industries frequently employ a pyramid structure, which includes elite or nucleus breeders at the top, one or more intermediate tiers of purebred or crossbred multipliers, and a final tier of commercial herds or flocks, or end users. Breeding companies supply semen from elite dairy bulls, with most elite cows owned by individual farmers, due to the prevalent use of AI in dairy cattle breeding. AI also enables commercial dairy producers to directly access elite genetic material, bypassing the multiplier tiers that are present in other sectors. The advancement and extensive utilization of technologies for semen collection, cryopreservation, and AI in dairy cattle have facilitated significant genetic enhancement in numerous countries via progeny testing, and—alongside associated embryo transfer technologies—have resulted in the global exchange of genetic material, rendering dairy cattle breeding a genuinely international pursuit [99].

Timed Artificial Insemination (TAI) boosts genetic selection, optimizes herd management, and increases profitability by advancing genetics and reducing the calving-conception interval [85,100]. Research demonstrates a strong correlation between the Genomic Daughter Pregnancy Rate (GDPR)- a genetic metric used to evaluate a cow’s fertility potential-and improved reproductive performance. Higher GDPR values are associated with increased pregnancy rates, shorter conception intervals, fewer services per conception, and reduced pregnancy losses. This renders GDPR an essential instrument for enhancing herd reproductive efficiency and selective breeding methodologies, especially for initial and recurrent inseminations [101,102].

Moreover, the integration of GDPR with TAI expedites genetic selection, optimizes synchronization efficiency, and promotes fertility control and overall herd profitability [97]. Despite the prevalent application of TAI to enhance herd pregnancy rates [103], decrease the interval from calving to the first service [104], and shorten inter-breeding intervals, substantial gaps persist in comprehending how emerging technologies such as genomics, transcriptomics, proteomics, and metabolomics can further refine hormone synchronization protocols for enhanced reproductive outcomes [105]. Omics technology have the potential to transform TAI by facilitating precision, focused reproductive techniques tailored to the biology of individual cows [97].

The application of transcriptomics, proteomics, and metabolomics to better understand the biological pathways underlying fertility. These approaches may lead to the identification of functional markers that improve the predictive power of genomic models for reproductive traits [4]. The integration of multi-omics data, genomic selection, and precision breeding tools is paving the way for personalized reproductive management in dairy herds [5]. By utilizing cutting-edge omics technologies, researchers and practitioners in the field of reproductive biology can gain a deeper understanding of the molecular mechanisms underlying fertility and develop more targeted interventions.

AI models can integrate multi-omics data (e.g., transcriptomics, metabolomics), phenotypic reproductive indicators (e.g., activity monitors, rumination time), and genomic SNP profiles to predict conception probability and calving intervals with high precision [106]. Embedding these predictions into GS programs enables earlier identification of genetically and physiologically superior breeding candidates, thereby shortening generation intervals while improving herd fertility.

4. Artificial Intelligence in Genomic Selection: Opportunities and Challenges in Dairy Cattle Breeding

4.1. Machine Learning and Deep Learning Approaches in Genomic Selection

The increasing availability of high-throughput genomic, phenotypic, and sensor-based data in modern dairy systems presents both opportunities and challenges in livestock breeding [107]. Traditional statistical methods often struggle to capture the complex, non-linear interactions among genetic, environmental, and management variables that influence economically and biologically important traits [108]. As a result, ML and DL approaches have emerged as transformative tools in GS pipelines, providing enhanced predictive accuracy, model flexibility, and integration capacity for diverse data sources [108,109].

ML encompasses a broad range of data-driven algorithms capable of learning patterns and making predictions from complex datasets without explicitly programmed instructions [110]. In dairy genomics, supervised learning models such as random forest, support vector machines, gradient boosting machines, and ensemble methods have been effectively applied to estimate GEBV’s, predict health-related traits (e.g., mastitis susceptibility, lameness), and identify animals with superior fertility or heat stress resilience. These models offer advantages over linear approaches by capturing epistatic interactions, gene–environment interplay, and sparse high-dimensional relationships typical of genomic datasets [111]. Although ML has obvious advantages in certain areas, it still faces many challenges in estimating the genetic breeding value of complex traits in animals. The interpretability of some models is low, which is not conducive to the adjustment of data, parameters, and features. Data heterogeneity, sparsity, and outliers can also cause data noise for ML. Furthermore, the lack of standardized data collection methods and inconsistencies in data quality across different sources can also hinder the accuracy of genetic breeding value predictions [112]. Despite these challenges, researchers are continuously working to improve the efficacy of ML in estimating genetic breeding values for complex traits in animals.

DL, a subset of ML based on artificial neural networks, enables the automatic extraction of hierarchical feature representations from raw data. Recent studies have demonstrated that convolutional neural networks and recurrent neural networks can be employed for trait prediction using genomic sequences, SNP arrays, transcriptomic data, or sensor time series (e.g., rumination, activity, body temperature) [109]. DL models have demonstrated competitive or superior performance compared to traditional genomic best linear unbiased prediction methods in estimating GEBVs for milk production, fertility, and disease resistance traits [113]. Narjice Chafai et al. [11] concluded that AI, particularly ML, holds significant promise for improving genomic prediction in animal breeding, but their successful application requires careful model selection, robust data, and integration with domain knowledge. Despite its potential, ML adoption in animal breeding is limited by data scarcity and quality issues, computational demands, lack of interpretability, limited expertise and infrastructure, especially in developing countries. The authors emphasize that ML should be seen as a complementary tool rather than a replacement for classical statistical methods. Hybrid approaches that combine biological knowledge with data-driven AI models are particularly promising [11]. Wilhelm Grzesiak et al. [114] stated that the development of AI is providing researchers and breeders with a variety of tools for the studies associated with dairy cattle farming, including genetic, environmental, and behavioral factors, and they conclude that ML, as a subset of AI, plays a transformative role in modern dairy cattle farming, offering powerful tools for analyzing complex, high-dimensional data that traditional statistical methods struggle to handle. ML enables more precise, automated, and data-driven decision-making across multiple domains of cattle management [114]. Palma et al. [115] emphasize that AI and ML are essential tools for modern dairy farm management and that there is a need for more real-time data integration to improve responsiveness. Simulation and optimization methods should be combined with ML to enhance decision-making. The authors highlight the underrepresentation of real-time data in current studies and advocate for its increased use to enhance responsiveness and decision-making accuracy in dynamic farm environments. Also the authors stress the importance of developing user-friendly software platforms and visualization interfaces to facilitate the adoption of AI tools by farm managers and veterinarians [115].

The integration of AI into the GS pipeline for dairy cattle begins with the acquisition of multi-source data, including genotypic information (e.g., single nucleotide polymorphisms), phenotypic records (such as milk yield, fertility, and health traits), and real-time sensor-derived parameters (including body temperature, activity, and rumination time) [116]. These datasets undergo integration and preprocessing before being analyzed by ML and DL models. The AI models are employed to predict GEBVs and to identify the relative importance of features contributing to desirable traits. The resulting outputs inform data-driven selection decisions aimed at improving animal health, production efficiency, resilience, and sustainability in modern dairy systems [10]. This approach allows for more accurate and efficient breeding practices, ultimately leading to healthier and more productive dairy cattle. By leveraging AI technology, farmers can make informed decisions based on data-driven insights, leading to increased profitability and sustainability in the dairy industry. Overall, the use of AI in dairy farming is revolutionizing the way in which breeding decisions are made, leading to improved outcomes for both farmers and animals alike. Future research should focus on enhancing decision support systems (DSS) by integrating AI-driven insights across multiple domains—feeding, breeding, health monitoring, and environmental management.

4.2. Milk Production and Reproductive Efficiency

Beyond general trait prediction, AI has shown considerable promise in optimizing milk production and reproductive performance through integration with genomic selection [97]. For milk production, ML algorithms such as gradient boosting machines and random forest have been applied to predict 305-day milk yield, fat percentage, and protein percentage using both SNP genotypes and longitudinal sensor data [117]. These models capture complex genotype–phenotype–environment interactions, enabling the identification of genomic regions linked to persistency of lactation and efficiency under heat stress conditions [118]. For instance, DL models incorporating rumination time, feed intake, and reticulorumen pH time-series data have improved the prediction accuracy of genomic breeding values for milk yield by more than 10% compared to conventional best linear unbiased prediction (BLUP) methods [119,120].

In reproduction, AI has been applied to forecast calving intervals, conception rates, and days open using genomic, phenotypic, and management datasets [121]. Recurrent neural networks have successfully modeled temporal reproductive events, such as estrus cycles, based on real-time activity, progesterone profiles, and environmental stress indicators [122]. Genomic prediction models enhanced with AI have been able to identify bulls and cows with superior fertility potential, even for low-heritability traits like conception to first service [123,124]. The integration of AI with GS not only improves the selection of reproductively efficient animals but also allows for early intervention strategies, such as optimized insemination timing or targeted nutritional supplementation, ultimately enhancing reproductive success rates and herd productivity [125].

4.3. Health Monitoring and Early Disease Prediction

While many AI applications in dairy cattle have emerged in the context of health monitoring, disease detection, and behavioral analysis, these same tools are increasingly being integrated into genomic selection pipelines. By converting complex phenotypic signals into reliable, standardized traits, AI models can enrich reference datasets used for genomic prediction. This ensures that GEBV’s reflect both traditional production traits and novel resilience, welfare, and environmental traits [126]. Importantly, AI-based models support the integration of multi-source data, including genotype, phenotype, metabolomics, sensor data, and environmental variables, thereby facilitating holistic evaluations of animal performance and robustness [126]. As AI models—especially DL—become more complex, such as Shapley Additive explanations (SHAP) and feature importance maps, are also being adopted to improve model transparency and identify key genomic regions or physiological indicators that drive trait expression [127]. Additionally, ML algorithms are increasingly used for real-time decision support in precision livestock farming, for example, predicting heat stress responses, calving times, or early disease onset using streaming sensor data [128].

Despite their promise, several challenges remain in applying ML/DL to GS [10]. These include data standardization, the need for large, labeled training sets, overfitting risks, and the interpretability of complex models. Nonetheless, as more curated reference populations and annotated multi-omics datasets become available, the synergy between AI and genomics is expected to accelerate breeding progress, improve animal welfare, and support sustainable dairy production [110].

Recent advances in ML have enabled the development of predictive tools for detecting and managing health disorders in dairy cattle. Various supervised learning algorithms—such as random forest, naïve Bayes, eXtreme Gradient Boosting (XGBoost), artificial neural networks, decision trees, and generalized linear mixed models—have been applied to diagnose clinical mastitis with promising results. For locomotion-related disorders like claw lesions and lameness, studies have employed random forest, K-nearest neighbors, decision trees, and naïve Bayes classifiers to distinguish affected animals from healthy counterparts. Several ML approaches have also been used concurrently to assess metritis outcomes and metabolic status in early lactation dairy cows [129].

Notably, Morteza et al. [129] utilized decision tree, random forest, and naïve Bayes models to investigate distinct metabotypes in transition cows, while Warner et al. [130] applied K-nearest neighbors, decision trees, and multilayer perceptrons to detect abnormal behavioral patterns associated with subacute ruminal acidosis. These algorithms offer the ability to model complex biological systems by identifying deviations from expected behavioral norms, thereby enabling early intervention [130]. These AI-derived phenotypes can be incorporated into multi-trait genomic evaluations, enabling selection for health and resilience traits that are otherwise difficult or costly to measure at scale.

Beyond disease detection, ML methods such as naïve Bayes, random forest, and artificial neural networks have also been widely used to predict milk yield, estimate genomic breeding values, and assess reproductive performance. Although traditional linear models like logistic regression remain in use, they may be less effective when analyzing large, heterogeneous datasets or detecting non-linear relationships [129]. The use of ML-driven health monitoring systems is transforming dairy farm management by enabling proactive, data-informed care for animals. These systems leverage sensor data, historical records, and predictive algorithms to support real-time decision-making [109]. Thus, the role of AI in advanced phenotyping is not separate from GS, but rather foundational—providing high-resolution, continuous trait data that, when integrated with genomic information, can accelerate genetic progress in health, climate resilience, and sustainability.

4.4. Machine Learning Applications in Heat Stress and Environmental Adaptation

Environmental stressors such as heat stress can significantly alter the physiological state, affective wellbeing, and natural behaviors of dairy cows. This makes the evaluation of heat stress a promising application area for ML approaches in precision livestock farming. While the physiological effects of heat stress are well established, ML models now enable prediction and prioritization of specific physiological responses under varying environmental conditions. Non-linear ML algorithms—particularly artificial neural networks and random forest—have demonstrated strong predictive performance for key physiological indicators, including respiration rate, skin surface temperature, and vaginal temperature, with reported R² values of 0.61, 0.85, and 0.47, respectively. These ML-predicted physiological traits can serve as novel phenotypes in GS programs, where their integration with genotypic data allows for the estimation of GBV’s for heat tolerance. This linkage enables the identification of animals with superior genetic resilience to environmental stressors, thereby supporting selection for climate-resilient herds. In addition, such models facilitate the ranking of environmental drivers influencing animal responses, with ambient air temperature often emerging as the dominant factor, while wind speed exerts comparatively limited impact. Importantly, ML-based models can also be used to define threshold values for environmental parameters (e.g., temperature–humidity index), which, when integrated into GS pipelines, inform both breeding and management decisions. By combining AI-driven phenotyping with GS, dairy breeding programs can accelerate the development of animals that maintain welfare, health, and productivity during periods of climatic stress [109].

5. Challenges and Considerations

Although genomic index evaluation has the potential to significantly enhance the health and economic viability of dairy cattle, it is imperative to address several obstacles to guarantee its long-term success and effective implementation.

The future of GS should involve a more integrative approach that combines genomic data with phenotypic, environmental, and management information to support sustainable breeding strategies. Additionally, international collaboration, open-access data sharing, and the development of breed-specific genomic resources should be prioritized to ensure equitable progress across global dairy systems. Whole-genome sequencing provides comprehensive genomic information, enabling the identification of rare and breed-specific variants. This deeper genetic insight is expected to significantly improve the accuracy of genomic predictions, particularly for complex traits and long-term profitability in dairy cattle breeding. The use of ML and AI is expected to revolutionize data analysis in GS.

While GS and AI each face their own technical hurdles, their combined application introduces additional complexities. One major challenge is the interpretability of advanced AI models such as DL, which often operate as “black boxes” and make it difficult for breeders to understand why certain animals are prioritized [131]. This can limit trust and hinder adoption in the field. Another concern is the risk of bias when AI models are trained on unbalanced or non-representative reference populations, leading to predictions that may perform poorly in different herds, breeds, or environments. Furthermore, transferring AI-driven genomic prediction models between countries or farm systems is complicated by variations in data collection methods, trait definitions, and genetic backgrounds. Standardized validation frameworks and cross-population benchmarking are therefore critical for ensuring model reliability and reproducibility. Recent efforts, such as the ExAutoGP framework, combine AutoML with SHAP to improve interpretability while maintaining high predictive accuracy [132].

5.1. Data Integration and Interpretation

Data quality is a well-known issue that has been acknowledged for a long time as a limitative factor, particularly in the field of genetic improvement of difficult-to-record characteristics such as mastitis, claw health, and many others [133]. The successful implementation of GS necessitates the incorporation of a variety of data sources, including genomic, phenotypic, environmental, and health records. Ensuring the quality and completeness of these data types, and harmonizing them remains a substantial technical challenge. Modern dairy cattle breeding increasingly relies on the integration of multi-source data—including genomic, phenotypic, pedigree, and environmental information—to enhance the accuracy and utility of genetic evaluations. This integration supports more precise selection for complex traits such as fertility, health, disease resistance or methane emissions and feed efficiency.

Data quality issues remain a major bottleneck, especially for traits that are low in heritability, costly or invasive to measure and subject to subjective or inconsistent recording. However, continued investment in infrastructure, standardization, and cross-disciplinary collaboration is essential to fully realize the potential of genomic technologies in sustainable dairy production.

Large-scale GS datasets with millions of SNPs and multi-modal phenotypes require significant computational resources for model training, validation, and deployment [134]. Smaller breeding organizations or farms may lack the infrastructure to process and store such data, highlighting the need for scalable, cloud-based genomic analysis platforms.

The integration of AI into GS pipelines further amplifies the demand for high-quality, harmonized data [135]. ML models, particularly deep neural networks, require vast amounts of labeled, standardized, and well-curated data to achieve optimal performance [136]. Inconsistent sensor calibration, missing genotype records, and variation in phenotype recording protocols can all introduce noise that reduces prediction accuracy. Moreover, the real-time streaming of sensor-derived phenotypes into genomic databases demands robust data infrastructure capable of handling continuous, high-frequency inputs [137]. Implementing FAIR (Findable, Accessible, Interoperable, Reusable) data principles across breeding programs and ensuring interoperability between farm management software, genomic databases, and AI analytics platforms are essential steps toward fully realizing the potential of this combined approach [138].

5.2. Future Issues

Compared to production traits, health and fertility traits have a lower heritability, which can impede genetic advancement. To enhance the accuracy of selection, it is necessary to employ more sophisticated genomic prediction models and larger reference populations.

GS and gene editing raise ethical concerns regarding the unintended consequences of genetic modifications, biodiversity, and animal welfare. Adherence to regulatory frameworks, ongoing ethical evaluations, and transparent communication with stakeholders are essential.

The genetic base may be inadvertently narrowed by intensive selection for specific traits, which in turn increases susceptibility to emergent diseases or environmental changes. While pursing desirable traits, breeding strategies must preserve genetic variability.

Although genomic tools have the potential to enhance sustainability, it is crucial to consider the environmental impact of technology production, data storage, and implementation. Furthermore, the social approval of genetically influenced breeding practices, particularly gene editing, may differ by culture and region.

To confront these obstacles, it is imperative that geneticists, veterinarians, data scientists, and policymakers collaborate. The full potential of genomic index evaluation in the dairy sector will be unlocked through the development of user-friendly genomic platforms, education, and ongoing research.

Automation-related sensor data is becoming more widely available, and related research is just now beginning to surface [133]. Precision farm management is gaining popularity due to factors like as increased herd size, enhanced efficiency, and the availability of sensors and automated image capturing [139]. Precision technology, such as activity sensors, feeding behavior recorders, automated milking robots, and computer vision, can produce extensive data to optimize genetic advancement for qualities associated with resilience and welfare [140,141]. This new era of automated sensor data presents both new opportunities and problems for evaluating and optimizing dairy cattle’s genetic potential. Since all applications of sensor data necessitate meticulous data quality validation, possibly with the aid of outside references, the first hurdle is data quality. Access to data is the second problem. In fact, in many systems, sensor data produced by automation is intended to be accessible on-farm. However, these data must also be made available off-farm to be useful, for instance, genetic enhancement. Since sensor data is frequently diverse and complicated by nature, a data integration and consolidation layer is necessary [133]. Automation-generated sensor data is becoming more widely available, which presents both new potential and several obstacles for dairy cow management and breeding [142]. AMS in conjunction with these modern monitoring technologies are widely regarded as a great way to acquire possibly unique dairy cattle genotypes [133].

Looking forward, the convergence of AI and GS holds significant promise for accelerating genetic gain and improving the adaptability of dairy herds to changing environments [143]. Emerging technologies such as federated learning offer the possibility of training powerful AI models without the need to centralize sensitive farm data, thereby addressing privacy and data ownership concerns [144]. Explainable AI (XAI) methods are also gaining traction, providing breeders and geneticists with transparent insights into which genomic features, environmental variables, or management factors drive a particular prediction [145]. This transparency not only improves trust but also supports informed decision-making.

Another promising avenue is the integration of multi-omics data—combining genomics, transcriptomics, metabolomics, and microbiome profiles—with longitudinal sensor data to enable truly individualized genomic evaluations [146]. AI can also simulate animal performance under different climatic scenarios, aiding in the selection of animals that are both high-yielding and climate-resilient [147]. While these prospects are compelling, their realization will require substantial investments in digital infrastructure, cross-disciplinary training, and the development of cost-effective AI–GS tools that are accessible to farms of all sizes.

The integration of AI and GS is revolutionizing dairy cattle breeding by enhancing the accuracy, speed, and efficiency of genetic evaluations. AI, particularly ML and DL, enables the modeling of complex, non-linear relationships between genotype and phenotype, while GS leverages genome-wide markers to predict breeding values. Together, these technologies promise transformative improvements in productivity, health, and sustainability. The incorporation of SHAP not only enhances model transparency but also facilitates the identification of key genomic loci, providing deeper biological insights that are critical for informed breeding strategies [148].

6. Conclusions

Genomic index evaluation represents a transformative approach in modern dairy cattle breeding, offering significant potential to improve animal health, productivity, and economic viability. By incorporating traits such as mastitis resistance, metabolic efficiency, fertility, and thermotolerance into selection indices, breeding programs are becoming increasingly effective at producing resilient, high-performing herds. Unlike interventions requiring repeated inputs, genetic improvements are cumulative and permanent, delivering long-term benefits in both animal welfare and operational efficiency.

Advances in genome-wide association studies, high-throughput sequencing, and gene editing technologies have enabled more precise identification of advantageous genetic markers. The integration of these tools with real-time herd monitoring and automated data systems enhances the precision of breeding decisions and herd management.

While challenges remain—particularly around cost, data integration, and ethical concerns—ongoing innovation and interdisciplinary collaboration are essential to realize the full benefits of GS. As genomic technologies become more accessible and refined, they will be central to meeting global demands for sustainable, efficient, and ethically responsible dairy production.

The continued evolution of AI methods—particularly explainable AI and transfer learning—will be central to refining genomic predictions for resilience, behavior, and disease traits. Their success, however, depends on standardized data pipelines, transparency in model outputs, and ethical breeding frameworks. The integration of AI-driven models into GS represents a critical step toward more intelligent, adaptive, and sustainable dairy cattle breeding systems.

7. Future Perspectives

Looking ahead, GS in dairy cattle will benefit from further integration of multi-omics data—including transcriptomics, epigenetics, and metabolomics—into predictive models to improve accuracy for complex traits such as disease resistance, fertility, and feed efficiency. The growing availability of sensor data from devices monitoring rumination, activity, temperature, and milk conductivity is opening new avenues for automated health monitoring, behavioral analysis, and precision breeding in dairy farming. However, the scientific exploration of how to best utilize this data, especially through AI and ML, is still in its early stages. Expanding genomic evaluations to underrepresented crossbred and indigenous populations will enhance genetic diversity and support breeding strategies tailored to local environments, particularly in regions affected by climate change.

Future breeding programs should prioritize the development of large-scale phenotyping initiatives for novel traits such as heat tolerance, methane emissions, and immune function. These efforts will enable more accurate genomic predictions and accelerate genetic progress. Concurrently, the application of advanced computational methods, including ML and AI-driven analytics, will improve the interpretation of genomic data and facilitate real-time breeding decisions.

Incorporating traits related to animal welfare and environmental impact into national and international selection indices is essential for aligning breeding goals with public expectations and sustainability targets. Ethical considerations, data governance frameworks, and equitable access to genomic tools—particularly in low- and middle-income countries—must be addressed to ensure responsible implementation.

Ultimately, the future of GS lies in harmonizing genetic innovation with precision management, climate resilience, and economic sustainability. By investing in robust research infrastructure, inclusive breeding programs, and cross sector collaboration, the dairy industry can realize the full potential of genomics to meet the challenges of tomorrow’s food systems.

The future of GS in dairy cattle will be increasingly shaped by the integration of AI-powered tools. ML and DL algorithms are uniquely positioned to manage high-dimensional genomic and sensor-based data, enabling more accurate trait prediction, better disease risk profiling, and optimized multi-trait selection under complex environmental conditions. Their incorporation into breeding strategy is expected to accelerate genetic progress while supporting resilient and data driven dairy production systems.