Medical imaging has become an important research field that embraces a great variety of sciences. Imaging is the central science of measurement in the diagnosis and treating diseases. The effort of technological progress has made possible human imaging starting from a single molecule to the whole body. A new strategy for suppressing random noise in medical images has been proposed in this research. This methodology is founded on a fuzzy filter which follows BM3D (Block Matching 3D) and fuzzy hybrid model. In this comprehensive study, we address the challenges posed by random noise in 2D and 3D images through a unique approach of image denoising. As high-quality medical imaging (Ultrasound, CT-Scan and MRI) is required to obtain a quick and accurate diagnosis. But the problem is that the image can frequently be contaminated by several types of noises including speckle noise, salt & pepper noise, and Gaussian noise. For RGB and grayscale images, some traditional fuzzy membership function is adeptly employed to eliminate these random noises. But here, our proposed research methodology also encompasses 2D and 3D techniques for medical and non-medical image denoising. This research contributes to advancing the field, offering insights into effective strategies for enhancing the quality and accuracy of images crucial for diagnostic and treatment purposes.