<p>Due to modern and rapid advancements in technological devices, medical imaging has gained popularity in the realm of medical care. The most important step in the medical image analysis is image denoising. In the healthcare sector, medical imaging techniques like Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, and ultrasound scanning are often used for diagnostics. The advancements in technology have made disease diagnosis considerably more precise than in the past. Medical imaging is crucial for disease detection, despite the presence of noise and other distortions in the images. When noise is present in the images, the minute details in the original image may be affected, which can result in an inaccurate disease diagnosis. To eliminate noise from images without compromising their features, different denoising techniques are needed. For noise reduction, experts have suggested a variety of procedures and methods. Each approach has benefits as well as disadvantages. Still, noise often affects these techniques, which could result in an inaccurate diagnosis. This survey paper provides different models that try to eliminate the noise present in the images. Additionally, it provides an inclusive survey of various noises and denoising methods. Moreover, the dataset descriptions and the experimental setup are analyzed in this review. The main intention of this survey is to perform a widespread investigation of different preprocessing models employed on medical images, which include CT, MRI, and PET. Finally, the research gaps and the challenges are provided for designing effective techniques in future.</p>