Hypertension is a multifactorial condition with both genetic and environmental causes. Although common, its genetic determinants remain unknown. Over the course of history, research has progressed from candidate gene studies often limited by small sample sizes and selection bias to large genome-wide association studies (GWAS) that have identified many common variants with minor effects. Polygenic risk scores (PGS), which sum the effects of multiple variants, have potential for risk prediction and personalized medicine, particularly when calculated from diverse datasets. However, important limitations remain, including restricted ethnic diversity, gene–environment interactions, and challenges in clinical application. Computational genomics and multi-omics integration offer opportunities to improve predictive models and elucidate disease pathways. Future progress will likely depend on large and diverse cohorts, coupled with sophisticated analytical approaches, with precision medicine and artificial intelligence poised to enable individualized risk estimation and facilitate more effective interventions to control hypertension.