Ceramsite aggregate concrete is renowned for its lightweight properties but often exhibits low compressive strength (CS), limiting its structural applications. This study introduces ultra-high-performance powder into Ceramsite aggregate concrete to enhance its CS while maintaining low density. Achieving optimal CS for this composite material requires a carefully designed mix. In this research, advanced machine learning (ML) techniques, including random forest, extreme gradient boosting, categorical boosting, light gradient boosting machine (LightGBM), natural gradient boosting (NGBoost), and adaptive boosting, were employed to predict the CS of lightweight Ceramsite aggregate UHPC (LWCA-UHPC). The study utilized 189 datasets from the authors' own experimental results for training and testing the models. The results demonstrated that NGBoost and LightGBM outperformed other models, with R² values of 0.9614 and 0.9645, respectively, in the testing phase. The models also showed low errors, with NGBoost having a mean absolute error (MAE) of 2.0624 and a root mean squared error (RMSE) of 2.9140, while LightGBM recorded an MAE of 2.0161 and an RMSE of 2.7778. Moreover, feature importance and Shapley additive explanations (SHAP) analyses revealed that the most influential factors affecting the CS of LWCA-UHPC were cement content, density, sand, and Ceramsite aggregate content, consistent with experimental findings. Additionally, a graphical user interface was developed to integrate the hybrid ML model into a user-friendly desktop tool, achieving an R² value of 0.9671 with a low mean squared error of 3.5204 when tested against experimental datasets. This tool not only enables practical applications in engineering but also helps reduce the need for extensive experimental work by providing accurate predictions of CS.