This study investigates the modeling of steel piles used in photovoltaic tracker foundations, integrating experimental load testing, physics-informed machine learning, and advanced numerical modeling. The work focuses on laterally loaded piles, for which field evidence shows that horizontal load tests are, in most cases, the governing design condition for tracker systems. A comprehensive experimental campaign of full-scale pile load tests was conducted to characterize soil–pile interaction and to calibrate numerical and data-driven models under realistic boundary conditions. The state of the art in pile analysis combines p–y methods, three-dimensional finite element models, and increasingly, machine learning techniques. However, conventional approaches often suffer from high computational cost, limited generalization, and uncertainty in soil parameter identification. To address these gaps, this study proposes a hybrid framework based on Physics-Informed Neural Networks (PINNs), enhanced with Kolmogorov–Arnold Networks (KANs), to represent soil behavior and pile response while preserving physical consistency. The methodology couples experimental data from horizontal pile load tests with governing differential equations embedded into PINNs, ensuring fidelity to soil–structure interaction mechanics. KANs are employed to extract interpretable correlations between soil parameters and response variables, offering a promising alternative for soil characterization and parameter inference. The trained PINN surrogates are then used in reliability analyses, enabling rapid Monte Carlo simulations with high accuracy compared to full numerical models. Results demonstrate that the proposed approach accurately reproduces nonlinear pile behavior under lateral loads, significantly reduces computational time, and improves robustness in reliability assessments. The framework highlights the strong potential of PINN–KAN models for scalable, physics-consistent, and efficient design and safety evaluation of photovoltaic tracker foundations.