This study investigates the optimization of heat transfer coefficients (HTC) during condensation processes by analyzing the effects of surface type, coolant flow rate and temperature with the goal of enhancing the efficiency of thermal management systems used in applications such as power generation, HVAC, and industrial cooling. Sixty experimental data sets were used to evaluate five coolant flow rates (0.5–2.5 L/min), four surface configurations and three temperature levels (100°C, 105°C, 110°C). An L9 orthogonal array based on Taguchi’s method was employed to minimize experimental trials. Signal-to-Noise (S/N) ratio analysis and Analysis of Variance (ANOVA) were used to determine the significance of each factor on HTC. The results showed that temperature had the most substantial effect on HTC variation, contributing 53.42%, followed by surface type (26.76%) and coolant flow rate (19.19%). Superhydrophobic surfaces with fins (SABWF) exhibited the highest HTC, particularly at higher temperatures and coolant flow rates. A regression model with an R-squared value of 99.37% was developed, indicating high predictive accuracy. These findings provide critical insights for optimizing condensation heat transfer in thermal systems, improving overall energy efficiency and system performance.