This paper analyzes the potential application of Physics-Informed Neural Networks (PINNs) in solving equations that describe thermal–electrical processes in thermoelectric systems. Combining machine learning with the laws of physics, the PINN method can serve as an alternative to traditional numerical methods, particularly in the context of the miniaturization of cooling systems, heat pumps, and systems that convert thermal energy (heat flow) into electrical energy (e.g., heat recovery), as well as the implementation of models in embedded systems. The article presents a model of thermoelectric equations, explains how PINNs work, provides numerical results, and assesses the advantages and disadvantages of the proposed approach.