The prediction of mortality in hospitalized COVID-19 patients using non-invasive and easily accessible measurements remains essential for improving patient outcomes, particularly in fast-paced clinical environments. The present study integrates generalized linear models (GLMs), fuzzy rule-based systems, and advanced machine learning algorithms—as support vector machines (SVMs), gradient boosting machines (GBMs), and random forests (RFs)—to predict COVID-19 mortality. The study was conducted on data from a Portuguese hospital, using patient age, length of stay, maximum oxygen administered, and timing of remdesivir (RDV) therapy as key predictors. Logistic regression provided high predictive performance, with an area under the curve (AUC) of 0.908, while the glmnet model achieved AUC = 0.892. Although ensemble methods such as RF (AUC = 0.922) and SVM (AUC = 0.952) demonstrated high accuracy, logistic regression remained competitive and it is superior due to its interpretability. Fuzzy models identified RDV as an important predictor (13.32% contribution), but with ambiguous effects. The logistic regression model found that delayed RDV administration increases mortality risk. These findings underscore the complexity of RDV impact on outcomes and highlight the importance of combining statistical models with machine learning techniques to enhance clinical decision-making for COVID-19 patients.