Electrical impedance tomography (EIT), a non-invasive, radiation-free, and real-time imaging modality of tissue conductivity changes, has become an important clinical tool. Three-dimensional EIT offers superior spatial characterization of lung function, but its image reconstruction remains challenging due to the severely ill-posed inverse problem and high sensitivity to measurement noise. This paper presents a generative model-enhanced deep image prior framework for unsupervised three-dimensiona