Significance Dusty plasma, a mixture of ions, electrons, and charged dust particles, is common throughout the universe. Understanding and modeling dusty plasma require precise knowledge of the complex interactions between dust particles, but machine learning (ML) is a promising approach for learning such interactions. In simulated data where the ground truth is known, ML models excel at making short-term predictions or inferring governing equations, yet there are few examples where new physical laws have been learned from real experimental data. Here, we demonstrate a scalable ML model that learns the complex interparticle forces from motion of particles in a laboratory dusty plasma. Our experiments and model reveal important discrepancies from common theoretical assumptions in dusty plasmas.