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Subsurface profile prediction using AI tools has yet to be widely adopted in mainstream geotechnical engineering practice. A key gap is a lack of research efforts to validate AI-based predictions of subsurface conditions using field data. Geosetta, an innovative initiative in North American geotechnical practice, has developed a comprehensive geotechnical data repository with data contributed from various US State Departments of Transportation, Canadian Transportation Ministries, and several consulting firms across North America. These data are used to develop a machine learning (ML) model for subsurface predictions in data-sparse areas. Geosetta predictions are provided in the form of boring logs with ranges of Standard Penetration Test (SPT) N-values and soil type. While not a substitute for thorough geotechnical site investigations, ML predictions offer a valuable initial understanding of expected subsurface conditions and may help ground-truth geophysical surveys that are conducted prior to (or without) invasive geotechnical testing. Consequently, improving accuracy and assessing the efficacy of these ML predictions is of great importance. In this study, we evaluate the accuracy of Geosetta predictions by comparing them with high-quality geophysical data collected from Multichannel Analysis of Surface Waves (MASW) testing and traditional geotechnical boring data collected on the campus of Pennsylvania State University. These predictions are evaluated against traditional boring logs and MASW data through (1) direct comparison of N-values and strata delineation and (2) computation of Site Class based on VS30. Our results highlight the potential of Geosetta predictions as a practical bridge between desktop studies and site investigations, providing engineers with valuable insights into expected subsurface conditions and aiding preparation for site characterization.