With the rapid advancement of artificial intelligence (AI), Learning Theory has emerged as a pivotal framework for exploring novel methodologies in applied mathematics. This presentation will discuss our recent findings on learning linear operators and their applications to inverse potential problems. Our theoretical analysis, supported by numerical experiments, confirms the efficacy of the proposed methods in addressing these complex inverse problems.