Abstract: Resistance spot welding is a complex physical process characterized by multifactorial interactions, leading to inherent uncertainty in the quality of spot welds. Therefor, this study proposed a novel approach that combined the whale optimization algorithm(WOA) with the XGBoost algorithm for predicting the nugget diameter in resistance spot welding.This predictive model leveraged crucial process parameters and material thickness as input features, while utilizing ultrasonic inspection-measured nugget diameter as the output variable. A database consisting of 102 experimental instances, each composed of seven input attributes, was constructed for training and testing the XGBoost regression model. Furthermore, the WOA optimization algorithm was employed to identify the optimal structure of the XGBoost model, and improved the WOA optimization algorithm to enhance the model prediction accuracy. The results show that the WOA-XGBoost model outperforms alternative standalone machine learning models, demonstrating higher prediction precision on the test set. This integrated model holds promise in assisting industries to scrutinize the influential factors affecting nugget diameter and offering a theoretical foundation for process parameter adjustments, thereby, potentially playing a pivotal role in automotive welding procedures.