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This paper highlights the advancements in artificial intelligence-assisted design of low-carbon cost-effective concrete, focusing on integrating machine learning-based property prediction with multi-objective optimization. Data collection and processing techniques, such as automatic data extraction, artificial data generation, and anomaly detection, are first discussed to address the importance of dataset quality. Strategies that capture physicochemical information of ingredients, including by-product supplementary cementitious materials and recycled aggregates, are then examined to enhance model generalizability. Various machine learning models\u2014from individual regression approaches to heterogeneous ensemble methods\u2014are compared for their predictive accuracy and robustness. Methods for hyperparameter tuning, model evaluation, and interpretation to ensure reliable and interpretable predictions are reviewed. Design optimization approaches are then highlighted, ranging from grid\/random searches to more sophisticated gradient-based and metaheuristic algorithms, aimed at minimizing carbon footprint, embodied energy, and cost. Future research avenues encompass (1) application-specific design frameworks that integrate critical objectives\u2014mechanical performance, durability, fresh-state behavior, and time-dependent properties such as creep and shrinkage\u2014tailored to specific structural and environmental requirements; (2) holistic design optimization that simultaneously refines mixture design and structural parameters; and (3) probabilistic approaches to systematically manage uncertainties in materials, structural performance, and loading conditions systematically.<\/jats:p>","DOI":"10.1007\/s10462-025-11182-1","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T03:38:24Z","timestamp":1746243504000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Review of AI-assisted design of low-carbon cost-effective concrete toward carbon neutrality"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8879-5431","authenticated-orcid":false,"given":"Soroush","family":"Mahjoubi","sequence":"first","affiliation":[]},{"given":"Rojyar","family":"Barhemat","sequence":"additional","affiliation":[]},{"given":"Weina","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Bao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,3]]},"reference":[{"key":"11182_CR1","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2020.119465","volume":"256","author":"J Abell\u00e1n-Garc\u00eda","year":"2020","unstructured":"Abell\u00e1n-Garc\u00eda J (2020) Four-layer perceptron approach for strength prediction of UHPC. 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