Machine learning (ML), as a subfield of AI, refers to the extraction of computational models able to reveal linear/non-linear relationships and patterns directly from data that would be difficult to obtain using common statistical methods. Predictive models are drawn, and the key is to remain bound on established physical laws [5]. Available ML algorithms dive deep into data, are being trained on data behavior and are capable of providing accurate predictions either in a supervised or an unsupervised learning approach. In supervised ML, input data characteristics are known a priori, while in unsupervised learning, the algorithm seeks hidden patterns inside unlabeled data, first [6]. ML algorithms are evolving on the basis of capturing data behavior while, in parallel, trying to eliminate less relevant input features, driven by explainability, which is currently a prerequisite for novel AI techniques [7]. The question of whether data-driven methods and ML fit to a problem or not is a subject of investigation. The existence of a certain amount of problem-specific data is critical in order to conduct the investigation. In practical applications, such as those in the construction and building sector, research data are acquired via expensive and time-consuming experiments, providing only a portion of the amount of data needed for an ML model to function effectively [8]. When simulation data are incorporated and data seem sufficient, excessive computational cost may arise if the number of input features entering the model is large. Moreover, possible input correlations may also exist, and further statistical analysis tools must be exploited [9]. This is a popular field for ML, suggesting efficient data preprocessing methods, application-specific algorithms based on simple architectures [10] or deep neural networks [11], and various output configurations, from numerical predictions to differential equation solving [12] and analytical equation extraction [13]. More specifically, in the field of construction and building industry, ML techniques have been employed to predict concrete properties that affect its strength and quality measures, such as the compressive strength, \(f_{c}\)[14]. The compressive strength may be affected by the concrete mix proportion [15], the addition of various substances, such as fly ash or ground granulated blast furnace slag [16], the amount of steel or polymer fibers in its content [17] or its structural and mechanical properties [18]. In all cases, the available databases have been widely exploited to apply novel ML methods to extract the properties of interest. Care has to be taken so that only high-quality data are exploited, preventing the consideration of erroneous, missing or redundant information [19]. In this paper, an extensive statistical analysis on the implied dataset and advanced feature engineering concepts for data curation are incorporated before data enter the ML computational flowchart. Feature engineering is capable of extracting relevant features from raw data and transforming them into a suitable format for ML models. It involves selecting, creating, and transforming variables that can be used to predict the outcome of interest in order to improve the performance of the model by making the data more relevant, informative, and predictive. The ML stage involves the application of 12 different ML algorithms, from purely linear to highly non-linear implementations, to predict \(f_{cc}\) values only from data. A key issue is the algorithm choice, which is vital for achieving increased prediction accuracy for the model. From an extensive computational analysis, it is concluded that the random forest (RF) algorithm has shown low error and superiority over prediction accuracy, reaching a value for the coefficient of determination of R\({}^{2}\) = 0.957, while linear-based algorithms fail to fit on the specific dataset.