<p>Adopting appropriate process parameters is crucial to produce high-quality squeeze-castings. To improve the efficiency and transparency of machine learning in the design of squeeze-casting process parameters, this study proposes an automated and interpretable artificial intelligence approach. The framework employs a stacked integrated model strategy to improve prediction accuracy, automatically optimizes the base model and its generated hyper-parameters, and ultimately interprets the model locally and globally using the Shapley interpretation technique. Application examples show that the relationship between material composition and optimal process parameters is extracted using the framework to obtain the implied relationship between material composition and process parameters in the black-box model, and the process automatically and efficiently completes the modeling and prediction tasks, which significantly improves the modeling efficiency. In addition, the accuracy of the tested performance on the squeeze-casting process dataset is better than that of other machine learning models such as Random Forest, and the predictive behavior of the mathematical model of the squeeze-casting material compositions and process parameters is explained by looking at the two dimensions, globally and locally. The methodology proposed promotes the development of new models for predicting the properties of squeeze castings and provides new ideas for squeeze-casting applications and material-process analysis. </p>