The growing interest in sustainable construction materials has prompted the investigation of alternative resources and sophisticated predictive techniques to enhance material performance. Waste foundry sand (WFS), a secondary product resulting from the metal casting procedure, present a viable alternative to natural aggregates, while the cement strength class (CSC) plays a crucial role in determining the properties of mortar. Although considerable research has been conducted on these elements separately, their combined influence on the compressive strength of mortar has not been thoroughly examined. This study aims to explore the interactive effects of varying percentages of WFS and different CSCs on the compressive strength of cement mortar, utilizing Gene Expression Programming (GEP), a cutting-edge machine learning approach. Compared to Artificial Neural Network (ANN) and other Machine Learning (ML) models, GEP offers enhanced transparency and robust predictive accuracy, making it more suitable for data-driven decision-making in sustainable construction. A comprehensive experimental dataset was created by varying WFS percentages (0%, 10%, 20%, 30%, 40% and 50%) and CSCs (32.5, 42.5, 52.5 MPa). The mix designs were evaluated under two conditions: random and sorted data modes, both with and without CSC as an input variable. GEP models were constructed to forecast compressive strength, incorporating WFS percentage, sand/ cement ratio (S/C), water/cement ratio (W/C), and CSC as primary inputs. The addition of CSC as an input variable significantly improved predictive accuracy, achieving a high correlation coefficient (R = 0.99) and a low root mean square error (RMSE = 2.3). The results underscore the necessity of considering both WFS and CSC in tandem within predictive models to effectively optimize mortar mix designs. By merging sustainable materials with advanced modeling methodologies, this research aids in resource conservation and the creation of high-performance, eco-friendly construction materials. The study provides a solid framework for engineers and researchers to advance material design and sustainability within the construction sector.

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