<p>Fins are used for various thermal applications, such as heat exchangers, electronic cooling systems, and aerospace components, as these increase the heat dissipation process. This study presents a combined experimental and statistical analysis to optimize the thermal performance of fins under forced convection, taking into account key process parameters such as airflow rate, fin base temperature, and atmospheric temperature. A series of nine experiments were carried out, each parameter set at three different levels to compute the heat transfer and fin efficiency in each configuration. Experiments were designed using a Taguchi analysis, and the results were examined using signal-to-noise (S/N) ratios and analysis of variance (ANOVA) to determine the significance and contribution of each parameter. Additionally, regression models were developed for predictive analysis and verified by confirmatory experiments. The results showed that fin base temperature most strongly affects heat transfer from the fin (96.76% contribution), whereas airflow rate has a significant effect on fin efficiency (97.82% contribution). The optimal parameters were calculated for maximum fin efficiency and heat transmission, and validation experiments proved the reliability of the prediction model, with minimal differences (&lt; 1%) between predicted and experimental values. The uniqueness of this work is that it integrates comprehensive statistical and experimental analysis for more accurate prediction of heat transfer and fin efficiency, with validation of the developed models via confirmatory experiments. This study provides substantial information that can be used to improve the design of fins and the thermal systems' performance.</p>