Climate change poses increasing challenges to livestock productivity and health, particularly in tropical and subtropical regions. Genomic technologies have emerged as powerful tools to enhance both disease resistance and environmental adaptability in animals. Several studies have demonstrated that integrating genomic selection, genome-wide association studies (GWAS), and functional genomics can accelerate breeding for resilience traits (Boettcher et al., 2015; Knap & Doeschl-Wilson, 2020). This review synthesizes evidence from seven recent studies addressing genomic adaptation and disease resistance in livestock species, including cattle, sheep, goats, and pigs. The selected works applied methodologies such as GWAS, SNP mapping, transcriptomic profiling, and selection signature analysis (Boettcher et al., 2015; Genomic Responses to Climatic Challenges in Beef Cattle, 2024; BMC Genomics, 2024; Genome Biol. Evol., 2020). These approaches were evaluated in the context of climate-related stressors such as heat tolerance, pathogen pressure, and environmental variability. Across species, genomic regions associated with heat stress tolerance and immune response have been consistently identified. For instance, heat-adapted cattle populations show selection signatures in genes such as TRPM8, HSP70, and SOD2 (Genomic Responses to Climatic Challenges in Beef Cattle, 2024). In goats, climate adaptation was linked to immune-related loci detected through landscape genomics (BMC Genomics, 2024). Likewise, studies in pigs and sheep revealed that resilience to polymicrobial diseases and weather variation is heritable and can be enhanced through genomic selection (Knap & Doeschl-Wilson, 2020; BMC Genom. Data, 2020). Collectively, these findings emphasize that genomic tools can identify adaptive variants and accelerate breeding for combined resilience to disease and climate stress. Genomic-based breeding therefore represents a sustainable strategy to improve livestock productivity and welfare under global climate change. Future directions should include the integration of multi-omics datasets and large-scale genomic databases to strengthen precision livestock adaptation strategies.