The increasing prevalence of vascular diseases poses significant challenges to global healthcare systems. Effective prediction and monitoring are essential for early diagnosis, personalized treatment, and improved patient outcomes. This study explores the application of prediction and monitoring techniques using correlation and regression models to analyze and manage vascular diseases. Correlation analysis identifies the relationships between key risk factors—such as age, obesity, hypertension, and lifestyle habits—and the onset of vascular conditions. Regression models, including linear, logistic, and multivariate approaches, predict disease progression, treatment efficacy, and mortality rates.The research leverages patient data to build predictive models that assess risk levels and monitor disease trends over time. Advanced tools, such as machine learning algorithms and time-series regression, enhance accuracy and provide actionable insights. These methods enable healthcare professionals to allocate resources effectively, design targeted interventions, and reduce the burden of vascular diseases on populations.The findings emphasize the importance of integrating predictive analytics into vascular disease management strategies, offering a scalable and data-driven framework to improve patient care and public health outcomes