Optimizing the performance and durability of engineering components requires efficient tribological system simulation. Gaining insight into such systems can be done by elastohydrodynamic lubrication (EHL) simulation or experimental methods. The latter is often costly and time-consuming. EHL, however, represents an alternative by providing understanding by incorporating hydrodynamic behavior, deformation, and contact mechanics. However, these simulations can be computationally intensive. Machine learning, especially models like physics-informed neural networks (PINNs), offers a promising solution by directly embedding the governing physical laws into neural network models. By integrating partial differential equations (PDEs) into their framework, PINNs enable fast and reusable simulations, making them particularly useful for tribological research. This work applies PINNs to model the hyperelastic deformation of a pneumatic seal, using the Piola–Kirchhoff stress equilibrium as the governing law to capture finite deformations. The seal material is modeled as a Neo-Hookean solid. The developed PINN framework for deformation can integrate with a previously established PINN for hydrodynamic lubrication to achieve a complete, accelerated EHL simulation. Results confirm the capability of PINNs to model deformation with high accuracy and reduced computational effort, highlighting their potential as efficient tools for simulating complex tribological systems.