Rich details in an image are constantly vital for medical image analysis to detect a broad extent of medical ailments. The diagnosis will be best served if the image is accessible in high resolution and the small details are preserved. Image super-resolution techniques based on deep learning can assist us in extracting spatial features from a low-resolution image captured with current technologies. The updated variant of the super-resolution technique known as Real Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), which produces 2D real-world images with great perceptual quality, is used in the present work. We investigate the suggested approach using four distinct medical image types: (1) brain MRI images from the BraTS dataset; (2) dermoscopy images from the ISIC skin cancer dataset; (3) cardiac ultrasound images from the CAMUS dataset; and (4) chest x-rays images from the MIMIC-CXR dataset. The employed architecture achieves improved visual results in comparison to the alternative innovative techniques for super-resolution. The observed findings are evaluated and contrasted both qualitatively and quantitatively with conventional approaches in terms of PSNR, SSIM, and MSE, and an improvement of up to 12% is obtained.