The prevalence of neurological disorders poses a challenge to modern healthcare, requiring advancements in diagnostic and prognostic methodologies. This study introduces a deep generative model that retroactively reconstructs magnetic resonance imaging data of human brains into their longitudinal counterparts, creating valuable methods for facilitating meticulous analyses of disease progression. The lack of imaging data on healthy individuals compared to those with brain degenerative disorders, coupled with the time-sensitive nature of some diseases, makes their early diagnosis and effective treatment complex. We demonstrate the model’s efficacy in generating anatomically accurate brain scans to aid in comprehending the dynamic nature of brain pathology, as evidenced by our mixed-method study: Our quantitative evaluation resulted in an outstanding Fréchet Inception Distance score of 5.801 and competitive performance in other key metrics compared to other state-of-the-art inpainting models. Our qualitative evaluation, conducted by two general radiologists and two neuroradiologists, yielded a Discrimination Success Rate of 51.67%, indicating the model’s success in generating realistic images. By integrating this methodology into clinical practice, we anticipate enhanced patient outcomes by personalizing precision medicine and emphasizing preventive strategies such as early and tailored therapeutic interventions.