INTRODUCTION: Prediction algorithms for prolonged mechanical ventilation (PMV) in the intensive care unit (ICU) have rarely incorporated detailed medication data, despite medications being important causal contributors to patient outcomes. The purpose of this study was to develop and validate PMV prediction models to assess the contribution of medication-related variables alongside established physiologic predictors. METHODS: In this retrospective cohort study, models were developed using data from a random sample of 318 adults admitted to ICUs within the University of North Carolina (UNC) health system who received mechanical ventilation for ≥ 24 h from October 2015 to October 2020. Validation was performed in two datasets: a temporally distinct cohort from UNC from June 2021 to June 2023, and a cohort from Oregon Health Sciences University from June 2020 to June 2023. Logistic regression and supervised, classification-based machine learning (ML) models [XGBoost, Random Forest, Support Vector Machine (SVM)] were trained on 30 demographic, clinical, laboratory, and medication-related variables. The primary outcome was area under the receiver operating characteristic (AUROC) of developed prediction models for the occurrence of PMV. RESULTS: The base logistic regression model with medication regimen complexity and severity of illness data added was the best-performing regression model, achieving an AUROC of 0.75. Random Forest and SVM ML models achieved AUROCs of 0.78. Model discrimination decreased modestly in external validation. Explainability analyses of ML models expectedly included severity of illness scores and respiratory indices among the most important features, but also consistently included the medication regimen complexity-intensive care unit (MRC-ICU) score and other medication metrics. Incorporation of medication data yielded modest improvements in overall discrimination and negative predictive value. CONCLUSIONS: Medication-related variables contributed incremental value to PMV prediction. ML methods provided marginal improvements over regression models. These findings highlight the potential value of medication data in prediction modeling for patient outcomes but emphasize the need to contextualize the value of complex models over simpler alternatives.