Adhesion strength is one of the most significant quality characteristics for coating performance. Heat treatment and sanding process parameters affect the adhesion strength. The aim of this study was to predict the adhesion strength using machine learning and optimization algorithms. Process factors were selected such as temperature, time, cutting speed, feed rate and grit size while coating performance index was selected as adhesion strength. Adhesion strength values of the specimens were determined by employing pull-off adhesion-type equipment. Firstly, central composite design with analysis of variance was used to create the experimental design and to determine the effective factors. Moreover, the main effect plot was used to determine the values of effective factors. Then, support vector machine (SVR) and extreme learning machine (ELM) were used to predict the adhesion strength. Finally, genetic algorithm was applied to optimize the parameters of SVM and ELM in order to improve the prediction accuracy. The proposed hybrid SVR-GA and ELM-GA approaches were compared with linear regression (LR), SVR and ELM. Experimental results showed that the proposed SVR-GA and ELM-GA approaches outperformed the LR, SVR and ELM in terms of prediction accuracy.