Elevated blood cholesterol is a significant risk factor for cardiovascular disease, which is the leading cause of death globally. A number of health, behavioral, and demographic factors can contribute to cholesterol levels. Machine learning-based predictive modeling of cholesterol can identify key predictors from patient data and produce personalized estimates of this vital risk marker. We detail the development of machine learning models to predict cholesterol levels and facilitate clinical risk assessment. Using a dataset of medical records with a range of features including patient age, blood pressure, smoking status, and maximum heart rate, we optimize predictive models for accurately projecting cholesterol levels. Through robust internal validation and out-of-sample assessment, we assess which specific attributes are significant statistical drivers of cholesterol predictions, informing feature selection. The best model achieves a R \(^2\) of 0.73, indicating over 70% of variance in total cholesterol is explained by the top 5 features. External validation on a standardized global dataset further tests generalization. This work demonstrates how machine learning algorithms fit on electronic health record data can enable practical tools for individualized risk scoring in clinical settings. By indicating the patient-specific factors most associated with increased cholesterol, we provide data-driven insight around placement onto preventative treatment pathways and living guideline-recommended lifestyles. The interpretability of predictive features also facilitates physician understanding to achieve integration of these models into routine practice. Overall, optimized and validated machine learning approaches for predicting cholesterol unlock personalized risk assessment from readily available clinical data.