Background: Polypharmacy is a growing issue that affects individuals of all ages yet is most prevalent among patients aged 65 and older with chronic comorbidities. Although integral to most treatment plans, pharmaceutical intervention may negatively impact one’s health when five or more medications are taken daily. Given the concurrent rise in elderly population and polypharmacy prevalence, it is vital that we better understand the impact that concomitant medication use has on this vulnerable segment of population.Purpose: This research examines the factors leading to polypharmacy among the elderly population and explores its various impacts on healthcare utilization. Data and Methods: This study uses Medical Expenditure Panel Survey (MEPS) Data. Fixed-Effects regression analyses examine relationships between predictive factors and polypharmacy, polypharmacy and expenditures, and polypharmacy and utilization. Classification models assess the ability of machine learning to correctly predict utilization within the sample population. Key Results: Aside from clinical indicators, demographic and socio-economic factors play a role in determining polypharmacy status. Polypharmacy risk is higher for women (1.088, p < 0.001), high income individuals (1.107, p < 0.01), and those covered by Medicaid (1.110, p < 0.001). Conversely, married individuals (0.930, p < 0.001) and non-Hispanic Blacks (0.864, p < 0.001) have reduced risks of polypharmacy. We find polypharmacy to be associated with higher total (p < 0.001), inpatient (p < 0.01), outpatient (p < 0.01), and prescription medical expenditures (p < 0.001) when holding other predictors constant. We find the risk of hospitalization to be higher for polypharmacy patients (RR: 1.592, p < 0.001) than nonpolypharmacy patients after controlling for multimorbidity and medication class. Lastly, machine learning algorithms classify admissions with an overall accuracy of 84.9%; however, a low true positive rate (TPR) of 41.7% and high true negative rate (TNR) of 96.5% indicate best performance is achieved in predicting non-admissions. Conclusion: Polypharmacy is associated with several non-clinical factors and has a statistically significant impact on medical expenditures and admissions. Though imperfect, predictive analysis methods improve our ability to identify patients at risk for admissions and present a potential opportunity for future applications aimed at reducing utilization and costs.