The paper introduces an enhanced imaging framework designed to address the quality limitations of ultrasound imaging systems, which often suffer from hardware-induced degradation. This framework leverages CycleGAN augmented with an attention mechanism and perceptual loss to effectively process both registered and non-registered ultrasound image pairs for medical applications. The attention mechanism selectively focuses on diagnostically significant regions, while the perceptual loss, derived from deep features of pretrained networks, ensures human-interpretable image quality. Comprehensive evaluations across five organ systems, using high-resolution reference standards, yielded impressive outcomes as it achieves SSIM of 0.2739, a LNCC of 0.8657, a PSNR of 15.2992, and LPIPS of 0.2592. These results highlight the framework’s ability to bridge quality gaps between different ultrasound devices while preserving critical anatomical details. The proposed technology holds significant promise for improving healthcare accessibility via portable imaging systems, although clinical validation remains essential for widespread adoption.