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Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. This research work focuses on the prediction of the surface roughness of additive-manufactured polylactic acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, explainable AI techniques are employed to enhance the interpretability of the machine learning models. The nine algorithms used in this study are Support Vector Regression, Random Forest, XGBoost, AdaBoost, CatBoost, Decision Tree, the Extra Tree Regressor, the Explainable Boosting Model (EBM), and the Gradient Boosting Regressor. This study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impacts of individual input parameters through explainable AI methods. The experimental results indicate that the XGBoost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XGBoost algorithm provides the most accurate predictions for surface roughness compared with other algorithms. This study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from explainable AI techniques. Full article