Historically, analysis and design methodologies for rigid airfield pavement systems have primarily relied on empirical equations derived mostly based the field performance. However, the FAA has implemented mechanistic-based approaches, including three-dimensional finite-element (3D-FE) procedures, for the design and analysis of rigid airfield pavements. This decision was made in response to the emergence of new wide-body aircraft and other design complexities, such as heavier aircraft and intricate gear configurations. On the other hand, the time-consuming nature of 3D-FE computation for analyzing multiple slabs subjected to aircraft and environmental loads has rendered routine design and analysis impractical. To address this issue, AI-based alternatives offer significant potential for producing accurate and rapid rigid pavement-response estimations compared to traditional FE-based design programs. Using AI-based modeling is a convenient option instead of conducting lengthy 3D-FE computations. In this paper, we introduce a recent FAA-sponsored research study conducted at Iowa State University (ISU) that utilizes AI-based alternatives to performing full 3D-FE computation for analyzing and designing rigid airfield pavement systems. The paper contains two case studies: (1) creating strong AI models to predict critical rigid airfield pavement responses and analyze top-down cracking behavior and (2) developing fast AI-based models to predict pavement foundation response and moduli for designing new and rehabilitated rigid airfield pavement structures. The capability of AI-based surrogate-response models for analyzing and designing rigid airfield pavement systems is successfully demonstrated and discussed in this paper.