Machine learning analysis of 2,911 proteins in 7,086 UK Biobank hypertension patients identified 10 distinct clusters based on differential expression of five key proteins: HAVCR1, PLAT, PTPRB, REN, and RTN4R. Four clusters showed significantly higher rates of cardiovascular and renal complications, while three demonstrated lower-than-expected complication rates. This represents a potential breakthrough in hypertension management, which currently treats the condition as a monolithic disorder despite affecting over 30% of adults. The findings suggest hypertension may actually comprise multiple mechanistic subtypes with different risk profiles and treatment responses. This could revolutionize precision medicine approaches, enabling clinicians to identify high-risk patients earlier and tailor therapies based on protein signatures rather than relying on traditional risk factors. The protein markers could also enhance clinical trial design by enriching for specific subtypes. However, this preprint awaits peer review and validation in independent cohorts. While promising for personalized cardiovascular medicine, the clinical utility depends on developing practical protein testing methods and confirming these clusters predict treatment responses.