Rising industrial carbon dioxide emissions necessitate utilization technologies. Carbon dioxide solidification captures carbon dioxide by reacting with alkaline compounds in concrete, improving its properties. This study integrates a life cycle assessment (LCA) model to evaluate carbon reduction potential with Machine Learning (ML) models to predict complex production dynamics. It investigates solidification mechanisms. Results show co-solidification and external solidification achieve reductions of 676.45 and 704.9 kilograms per tonne, respectively, with notable environmental benefits. A comparison of three predictive models, namely Feedforward Neural Networks (FNN), Polynomial Regression, and Support Vector Regression, confirms that FNN is the optimal choice. It exhibits a lower mean absolute error (791) and a higher coefficient of determination (0.91). SHAP analysis revealed that ‘Coal consumption’ and ‘Electricity consumption’ were the primary drivers of the FNN prediction, confirming the model’s reliance on essential energy inputs, while the ‘date’ feature exerted minimum influence. Projections indicate China’s 2024 concrete production emissions could be 4.23 billion tonnes via synergistic curing, versus 4.37 billion tonnes with conventional external curing. Case and visual analyses further validate carbon dioxide curing’s advantages in improving concrete performance and cutting energy use.