The rising demand for high-resolution magnetic resonance imaging (MRI) presents clinical concerns since existing imaging systems have limitations that make medical information less reliable. This research addresses MRI super-resolution, which occurs when low-resolution images cause inaccurate diagnoses. Our novel approach uses deep external learning, a guided residual density network (GRDN), and multimodal image priors to increase MRI clarity. We train the model to learn robust features that enable high-fidelity restoration using a variety of complementary imaging approaches. The GRDN leverages low-level features to better retrieve tiny details and patterns in high-resolution pictures. We demonstrate that the suggested strategy outperforms existing tactics in both quantitative and qualitative terms using standard datasets. The findings suggest that our strategy increases image quality, retains anatomical details, and simplifies diagnosis. This approach may enhance MRI quality, thereby advancing medical imaging. Patients may benefit from more accurate evaluations and findings.