Genome-wide association studies (GWAS) have become a powerful tool for uncovering the genetic basis of complex traits in maize. Maize, with its extensive genetic diversity and rapid linkage disequilibrium (LD), is an ideal candidate for GWAS. These studies have identified numerous genetic loci and candidate genes associated with complex traits, including responses to abiotic and biotic stresses. GWAS have enhanced our understanding of agronomic traits and environmental stress resilience in maize, and have facilitated the development of marker-assisted selection in molecular breeding programs. The integration of omics data, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, has enriched our understanding of complex traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. However, GWAS face challenges such as low-frequency variant analysis, missing heritability, and the need for large and diverse populations. Despite these challenges, GWAS continue to provide valuable insights into the genetic mechanisms underlying complex traits in maize, and are instrumental in advancing maize breeding programs. The future of GWAS in maize research lies in addressing these challenges and leveraging the power of omics data to further enhance our understanding of maize genetics and improve breeding strategies.Genome-wide association studies (GWAS) have become a powerful tool for uncovering the genetic basis of complex traits in maize. Maize, with its extensive genetic diversity and rapid linkage disequilibrium (LD), is an ideal candidate for GWAS. These studies have identified numerous genetic loci and candidate genes associated with complex traits, including responses to abiotic and biotic stresses. GWAS have enhanced our understanding of agronomic traits and environmental stress resilience in maize, and have facilitated the development of marker-assisted selection in molecular breeding programs. The integration of omics data, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, has enriched our understanding of complex traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. However, GWAS face challenges such as low-frequency variant analysis, missing heritability, and the need for large and diverse populations. Despite these challenges, GWAS continue to provide valuable insights into the genetic mechanisms underlying complex traits in maize, and are instrumental in advancing maize breeding programs. The future of GWAS in maize research lies in addressing these challenges and leveraging the power of omics data to further enhance our understanding of maize genetics and improve breeding strategies.