This study evaluates whether machine learning can improve cardiovascular risk prediction using routine clinical data. It compares four machine learning algorithms—random forest, logistic regression, gradient boosting machines, and neural networks—with the American College of Cardiology (ACC) guidelines to predict the first cardiovascular event over 10 years in a cohort of 378,256 UK patients. The study found that machine learning algorithms significantly improved prediction accuracy, with neural networks achieving the highest area under the receiver operating characteristic curve (AUC) of 0.764, compared to the ACC algorithm's AUC of 0.728. The neural network model correctly predicted 355 more patients who developed cardiovascular disease than the ACC algorithm. It also showed higher sensitivity (67.5%) and positive predictive value (18.4%) for identifying cases, and higher specificity (70.7%) and negative predictive value (95.7%) for identifying non-cases. The study highlights that machine learning can better incorporate complex interactions between risk factors and improve the accuracy of cardiovascular risk prediction, leading to more effective preventive treatment for those at risk while avoiding unnecessary treatment for others. The results suggest that machine learning has the potential to enhance cardiovascular risk prediction in primary care settings.This study evaluates whether machine learning can improve cardiovascular risk prediction using routine clinical data. It compares four machine learning algorithms—random forest, logistic regression, gradient boosting machines, and neural networks—with the American College of Cardiology (ACC) guidelines to predict the first cardiovascular event over 10 years in a cohort of 378,256 UK patients. The study found that machine learning algorithms significantly improved prediction accuracy, with neural networks achieving the highest area under the receiver operating characteristic curve (AUC) of 0.764, compared to the ACC algorithm's AUC of 0.728. The neural network model correctly predicted 355 more patients who developed cardiovascular disease than the ACC algorithm. It also showed higher sensitivity (67.5%) and positive predictive value (18.4%) for identifying cases, and higher specificity (70.7%) and negative predictive value (95.7%) for identifying non-cases. The study highlights that machine learning can better incorporate complex interactions between risk factors and improve the accuracy of cardiovascular risk prediction, leading to more effective preventive treatment for those at risk while avoiding unnecessary treatment for others. The results suggest that machine learning has the potential to enhance cardiovascular risk prediction in primary care settings.