Electromagnetic Tomography (EMT) is a non-destructive imaging modality that reconstructs internal conductivity or permittivity distributions by solving an ill-posed inverse problem. Traditional reconstruction methods, such as Linear Back Projection (LBP) and Conjugate Gradient (CG), often suffer from low accuracy, strong artifacts, and poor edge preservation due to ill-conditioned sensitivity matrices and noise amplification. In recent years, deep learning has provided new solutions for EMT image reconstruction through its strong nonlinear fitting ability and multi-scale feature extraction capability. With the development of encoder–decoder structures, skip-connection strategies, and attention mechanisms, a series of neural-enhanced EMT reconstruction models have emerged, effectively improving artifact suppression, multi-target discrimination, and real-time performance. Among them, U-Net-based frameworks and attention-augmented variants, such as CBAM-U-Net, demonstrate significant advantages in boundary restoration, feature refinement, and noise robustness. This review summarizes the major research progress of deep learning in EMT image reconstruction, outlines the evolution from hybrid shallow models to specialized deep architectures, and discusses future directions for multimodal fusion and advanced neural frameworks.