This study innovatively combines Isogeometric Analysis (IGA) with Machine Learning (ML) to assess strip footing bearing capacity on dual clayey layers. Overcoming limitations of conventional methods with small sample sizes, our research generates a dataset of 10,000 samples, allowing a thorough exploration of diverse soil profiles. Facilitated by ML, 10,000 IGA analyses using upper bound limit analysis unveil intricate patterns and relationships previously obscured. The key innovation lies in harnessing big data and employing advanced data visualization, particularly 2D and 3D Partial Dependency Plots (PDPs). These PDPs visually showcase the impact of factors such as upper layer thickness, cohesion ratios, shear strength profiles, footing depth, and foundation roughness on bearing capacity. Offering intuitive insights, these visualization tools enhance comprehension, aiding informed decision-making in design and construction. Engineers and geotechnical experts receive a precise predictive tool, optimizing strip footing performance on clayey soil layers. Moreover, this research contributes to advancing geotechnical engineering by enriching fundamental knowledge of load-bearing characteristics. In summary, the fusion of big data, advanced visualization, and upper bound limit analysis, exemplified by PDPs, signifies a substantial leap in geotechnical engineering, impacting design, construction, and infrastructure development. [ABSTRACT FROM AUTHOR]