ZnNi alloy coating is commonly used to enhance the corrosion resistance of steel. The percentage of Ni should be maintained between 12% and 14% in the coating for best corrosion performance. The response surface design (RSD), polynomial regression (PR), support vector regression (SVR), XGBoost regression (XGB), K-nearest neighbours regression and Gaussian process regression (GP) algorithms have been used to predict the ZnNi alloy coating thickness and Ni % amount in the coating. As statistical indices mean square error (MSE) and correlation coefficient (R²) were used to compare the models. The results of the analysis show that the XGB algorithm gives the best estimation for both ZnNi thickness and Ni%. A high correlation was observed between the predicted values and experimental results. R² values of 0.87 and 0.81 were acquired for ZnNi thickness and Ni %, respectively, using the XGB algorithm. This study has proved that the machine learning algorithm is a promising method to predict the ZnNi coating thickness and Ni % in the alloy based on the composition of the ZnNi electroplating bath.