Physics-Informed Neural Networks (PINNs) have been recognized as a promising approach in scientific machine learning, combining deep neural networks and governing equations of physical systems [1]. This paper presents a parameterized Physics-Informed Neural Network architecture to model elastically deformable materials with different material properties under various initial and/or boundary conditions. The approach has the potential to solve problems related to predicting displacements and stresses in a wide range of elastic materials and boundary value problems without using external numerical data. Instead, it uses a physics-informed loss function derived from partial differential equations of mechanical equilibrium, constitutive equations of linear elasticity, and boundary value problems. Unlike traditional numerical approaches, in which changing material parameters requires re-computation of solutions in the entire computational domain, this proposed approach uses a trained parameterized PINN as a universal surrogate to solve all possible elasticity problems in a matter of seconds. This advantage also increases the popularity of this approach in solving various inverse problems and real-time applications. The proposed approach in this paper was implemented as a numerical experiment using the PyTorch environment and PINN-2DT. This environment combines computer science and telecommunications aspects, such as deep learning architectures, loss function optimization, and numerical algorithms, along with materials science aspects, such as continuum mechanics and elasticity theory. The proposed approach also contributes to next-generation surrogate models, which may become building blocks in various real-time systems and intelligent control systems in computational mechanics by aligning itself with current global trends in physics-based AI and scientific machine learning. [1] A. D. Mouratidou, G. A. Drosopoulos, and G. E. Stavroulakis, “Ensemble of physics-informed neural networks for solving plane elasticity problems with examples,” Acta Mechanica, vol. 235, no. 11, pp. 6703–6722, 2024, doi: 10.1007/S00707-024-04053-3.