This paper proposes an ultrasound data denoising and image enhancement method combining fuzzy logic and genetic algorithm optimization to improve imaging quality and diagnostic accuracy. Ultrasound imaging, widely used in medical and industrial testing, is often degraded by speckle, thermal and quantization noise, reducing clarity and contrast. To address this, a fuzzy system was designed including fuzzification interface, rule base, inference engine, and defuzzification output, with Gaussian membership functions adaptively tuned through genetic algorithms. Experiments were conducted on the Duke Abdominal Ultrasound Dataset and the BUS Breast Ultrasound Dataset, and the results were evaluated using signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Compared with mean filter, median filter, wavelet transform, adaptive Wiener filter, and convolutional neural networks, the proposed method achieved consistent improvements. For instance, on abdominal ultrasound images, the fuzzy algorithm increased SNR by approximately 5.5% and PSNR by 6.4% compared with CNN, while SSIM improved by 2.3%. On breast ultrasound data, the method yielded a 5.6% higher SNR, a 5.5% higher PSNR, and a 2.3% higher SSIM than CNN. These results show integrating fuzzy logic and genetic optimization offers an effective, efficient, generalizable ultrasound image enhancement strategy, with potential clinical and industrial use.