When a patient is prescribed medication from healthcare providers, their net copayment at the pharmacy is determined by a complex system involving many factors, such as the specific drug treatments and the patient’s insurance. Currently, patients and doctors do not have a method of checking expected copayment costs before prescribing medication. Machine learning presents considerable opportunities to improve patient-facing drug recommendations. In this project, we survey many regressors for predicting copayment costs based on patient insurance plan, prescribed drug, patient diagnoses, and other factors. With this, we hope to build the foundations for future systems that will inform doctors and patients about potential costs of medication before prescription to help patients work with doctors to find affordable treatments for their conditions.