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This manuscript delineates the assembly and structure of an extensive dataset encompassing more than 2500 self-consolidating concrete (SCC) mixtures, meticulously compiled from 176 scholarly sources. The dataset has been subjected to a thorough curation process to eliminate feature redundancy, rectify transcriptional inaccuracies, and excise duplicative entries. This refinement process has culminated in a dataset primed for advanced data-driven inquiries within the SCC research domain, marking a novel contribution to the field. The dataset serves as a robust foundational resource, poised for subsequent augmentations and stringent applications in data-centric studies. It facilitates a detailed characterization of SCC properties, potentially through the implementation of machine learning algorithms, or serves as a comparative benchmark to assess the performance across diverse SCC formulations. In conclusion, the dataset serves as a crucial resource for scholars engaged in studying SCC and similar substances. It offers deep insights into the ecological benefits of substituting conventional Portland concrete with SCC alternatives. This compilation not only advances the understanding of SCC properties but also contributes to the broader conversation about sustainable construction practices.