Magnetic Resonance Imaging (MRI) is very impor-tant in the detection of neurological defects because it possesses high resolution that enables good visualization of soft-tissue structure. However, diagnostic clarity is often hindered by low-resolution scans due to the short time of acquisition, motion artifacts and hardware constraints. Recent advances in deep learning, such as Enhanced Super-Resolution Generative Ad-versarial Networks (Real-ESRGAN), have demonstrated strong capabilities of perceptual-driven image enhancement.This paper discusses Real-ESRGAN-based MRI super-resolution strategies, their architectural advantages and clinical potential benefits, in preserving fine anatomical and pathological details much better than CNN-based and conventional interpolation methods. We also present a conceptual AI-enabled deployment framework, where Real-ESRGAN is handled by a clinician support chatbot for application in web-based interaction, tele-radiology accessibility and diagnostic help. Clinical validation including metrics such as PSNR, SSIM,LPIPS and sFRC is investigated. The study emphasizes the need for interpretable, regulation-ready models to bridge AI-driven MRI enhancement with real-world diagnostic workflows.