Embodied carbon (EC) in building materials poses a significant challenge to decarbonizing the construction industry, necessitating data-driven methods for transparent and informed decisions. This study systematically reviews the use of artificial intelligence (AI) and explainable AI (XAI) in assessing EC and promoting low-carbon building materials. A PRISMA 2020–based review and bibliometric analysis of Scopus publications from 2015 to June 2026 explored trends, geographic distribution, research themes, AI/XAI methods, and their applications in EC assessment and material decarbonization. Research has notably increased in 2023, highlighting AI’s growing role in life cycle assessments and material decarbonization. Machine learning methods such as neural networks, random forests, support vector machines, gradient boosting, and deep learning are increasingly used for EC prediction, material performance evaluation, and optimization. However, XAI applications are limited, with more focus on prediction accuracy than model transferability. Key XAI techniques include Shapley Additive exPlanations, partial dependence plots, sensitivity analysis, and feature importance analysis. The review identifies limitations such as regional data gaps, limited model explainability, limited database integration, and limited empirical validation. A conceptual framework is proposed combining localized life cycle data, AI prediction, XAI interpretation, optimization, and decision support. The review concludes that XAI can enhance transparency and aid low-carbon material decisions, though further validation across laboratories, manufacturing, and regions is needed for practical implementation.