Understanding how space plasmas behave—such as those around Earth—is important for predicting space weather and its effects on satellites and communication systems. However, since we can’t directly see these invisible structures, scientists rely on measurements from spacecraft to reconstruct what’s happening in space. This study introduces a new way to do that using a type of machine learning (ML) called physics-informed neural networks (PINNs). PINNs combine scientific knowledge (in this case, the physics equations that describe plasma behavior) with deep learning. Unlike older methods that require strict assumptions and negligible measurement errors, this new approach can flexibly recreate the surrounding plasma structures by “learning” from both the data and the laws of plasma physics at the same time. We tested this method on synthetic data and on a real event in Earth’s magnetic tail, where magnetic reconnection—an important space plasma process—was observed. The method successfully recovered key features of this event and even estimated how fast the reconnection occurred and how the plasma’s electrical resistivity varied in space. Overall, this ML-based approach opens up new possibilities for understanding complex space environments using spacecraft data, even when traditional analysis methods might not fully apply.