This study explores the interrelationship between pump performance, system efficiency, and noise/vibration levels by analyzing the influence of pump frequency and outlet pressure. System efficiency predictions were conducted utilizing both the Response Surface Method (RSM) and advanced machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and XGBoost. The comparative analysis revealed that ANN provided the highest prediction accuracy with an R2 value of 0.946, Root Mean Square Error (RMSE) of 1.2% and Mean Absolute Percentage Error (MAPE) of 2.32%. However, when predicting system efficiency using external data inputs, RSM outperformed other models, achieving an R2 value of 0.96 and a mean error rate of 3.84%. Optimization via RSM was performed for target flow rates of 35, 40, and 45 m3 h−1, with the optimal flow rate determined at 35 m3 h−1, corresponding to a system efficiency of 42%. To validate these optimization results, experimental tests were conducted, revealing a flow rate of 35.4 m3 h−1 and system efficiency of 42.95%, with error margins of 1.12% and 2.21%, respectively. The study demonstrates that RSM is a robust and effective tool for optimizing pump system performance, offering practical applications in improving energy efficiency and operational stability in pumping facilities.