Picture denoising is an important step in image evaluation, which enhances the visual quality while retaining the significant features. The core of the following study will propose a new picture denoising algorithm by combining FCM and the Modified Fuzzy C-Means (MFCM) clustering algorithm. The proposed method will address typical denoising challenges, such as the edge preservation and noise suppression dilemma, which always happens in images with fine textures and structures. The Modified Fuzzy C-Means clustering method improves the original Fuzzy C-Means algorithm by incorporating spatial constraints and adaptive membership functions. It provides a more accurate detection and categorization of noise in noisy images. The Fractional Convolutional Method, on the other hand, inspired by the use of fractional calculus, gives higher denoising accuracy by making use of fraction-order filters, which may reflect much finer qualities in pictures. The proposed method's performance is much better compared to conventional denoising algorithms like Gaussian filtering and regular convolution techniques on the experimental grounds with respect to the evaluations in terms of metrics like SSIM and PSNR. Besides, qualitative experiments also show that the performances of the proposed method in shape preservation and fine textures are superior, hence potentially enough for applications such as remote sensing, health imaging, or, in general, for domains where picture restoration requires high fidelity.