Super-resolution (SR) techniques present a suitable solution to increase the image resolution acquired using an ultrasound device characterized by a low image resolution. This can be particularly beneficial in low-resource imaging settings. This work surveys advanced SR techniques applied to enhance the resolution and quality of fetal ultrasound images, focusing Dual back-projection based internal learning (DBPISR) technique, which utilizes internal learning for blind super-resolution, as opposed to blind super-resolution generative adversarial network (BSRGAN), real-world enhanced super-resolution generative adversarial network (Real-ESRGAN), swin transformer for image restoration (SwinIR) and SwinIR-Large. The dual back-projection approach enhances SR by iteratively refining downscaling and super-resolution processes through a dual network training method, achieving high accuracy in kernel estimation and image reconstruction. Real-ESRGAN uses synthetic data to simulate complex real-world degradations, incorporating a U-shaped network (U-Net) discriminator to improve training stability and visual performance. BSRGAN addresses the limitations of traditional degradation models by introducing a realistic and comprehensive degradation process involving blur, downsampling, and noise, leading to superior real-world SR performance. Swin models (SwinIR and SwinIR_large) employ a Swin Transformer architecture for image restoration, excelling in capturing long-range dependencies and complex structures, resulting in an outstanding performance in PSNR, SSIM, NIQE, and BRISQUE metrics. The tested images, sourced from five developing countries and often of lower quality, enabled us to show that these approaches can help enhance the quality of the images. Evaluations on fetal ultrasound images reveal that these methods significantly enhance image quality, with DBPISR, Real-ESRGAN, BSRGAN, SwinIR, and SwinIR-Large showing notable improvements in PSNR and SSIM, thereby highlighting their potential for improving the resolution and diagnostic utility of fetal ultrasound images. We evaluated the five aforementioned Super-Resolution models, analyzing their impact on both image quality and classification tasks. Our findings indicate that these techniques hold great potential for enhancing the evaluation of medical images, particularly in development countries. Among the models tested, Real-ESRGAN consistently enhanced both image quality and diagnostic accuracy, even when challenged by limited and variable datasets. This finding was further supported by deploying the ConvNext-base classifier, which demonstrated improved performance when applied to the super-resolved images. Real-ESRGAN's capacity to enhance image quality, and in turn, classification accuracy, highlights its potential to address the resource constraints often encountered in these settings.