Geotechnical Engineering is a data-driven specialty that models the behavior of soil and rock as engineering materials. Due to their nature, however, much effort is directed toward defining the material properties and extent of various soil and rock on a site (buildings, bridges, dams) or region (transportation infrastructure). The great extent and variability of soil as an engineering material have led to the development of material, field exploration, and laboratory testing databases. Some have become national in scope, while others have expanded along specialist lines (e.g., earthquakes, landslides). This paper presents a typical application that investigates the impact of swelling clays and discusses its integration into the BENIP framework, with an emphasis on its role in advancing sustainability in geotechnical engineering. The study employs artificial neural networks as a modeling tool to predict crucial parameters such as dry density and in-situ confining stress, which directly influence volumetric changes in the soil. By minimizing these changes, potential damage associated with swelling clays, including ground movement, foundation deterioration, and infrastructure instability, can be mitigated. The results of the study exhibit promising outcomes, signifying the potential effectiveness of the proposed neural network models in promoting sustainability in geotechnical engineering.