This paper investigates the mathematical optimization of machine learning hyperparameters in the context of early chronic kidney disease (CKD) detection. This study compares two high-performing ensemble classifiers - Random Forest and XGBoost - under five tuning strategies: default scikit-learn settings, Grid Search, Random Search, a Genetic Algorithm (GA), and Ant Colony Optimization (ACO). Logistic regression, k-nearest neighbors, support vector machines, decision trees, and Naive Bayes were evaluated as baseline comparators under default settings only; their tuning results are reported in the supplementary material. The main experimental comparison focuses on RF and XGBoost, for which full five-strategy benchmarks, sensitivity analysis, and runtime profiling are available. Grid Search and Random Search are shown to be impractical at scale: for Random Forest, Grid Search evaluated 432 configurations in 4.2 hours and Random Search evaluated 200 in 1.8 hours, neither reliably reaching high-sensitivity solutions. GA-optimized Random Forest and XGBoost reached AUC-ROC values of 0.963 and 0.968, respectively, while ACO-optimized variants achieved 0.969 and 0.975. Critically, sensitivity for the CKD class the clinically most important metric rose from 0.864 to 0.924 (ACO) for Random Forest. Paired Wilcoxon signed-rank tests confirmed that all GA/ACO improvements over Grid Search are statistically significant (p < 0.01). The results establish that evolutionary optimization offers a practically favorable accuracy-to-runtime trade-off for medical classification tasks where the cost of false negatives is high.