Accurate prediction of serum creatinine levels is of significant clinical importance for early assessment of kidney function. This study presents a comprehensive comparative analysis of five machine learning algorithms for predicting creatinine from routine blood parameters. Using a dataset spanning multiple years (2020–2024) with clinical blood test results, we developed and evaluated models based on Random Forest, Gradient Boosting, Elastic Net, Lasso, and Neural Network approaches. Performance metrics included root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and cross-validated R2. Gradient Boosting demonstrated superior performance with an RMSE of 12.00 mol/L and R2 of 0.67, followed closely by Neural Network (RMSE: 12.27 mol/L, R2: 0.65) and Random Forest (RMSE: 12.40 mol/L, R2: 0.65). Feature importance analysis revealed blood urea nitrogen, age, and potassium levels as the strongest predictors of creatinine values. The study presents a robust predictive framework that could support early detection of kidney dysfunction, potentially enabling timely intervention before significant renal damage occurs. This approach may prove particularly valuable in resource-limited settings where specialized kidney function tests are not readily available.