Automated interpretation of image data provided by Synthetic Aperture Radar (SAR) using several machine learning and deep learning techniques have become an emerging research area in the computer vision field in recent years. But processing these images without performing speckle noise removal often leads to the production of highly erroneous results. These images are inherently subjected to speckle noise which are formed due to constructive and destructive addition of backscattered electromagnetic waves while mapping them into resolution cells of finite dimensions during image formation. To overcome this limitation, in this work, we have designed an adaptive weighted image despeckling strategy based on fuzzy logic (FL). The considered strategy allows to remove speckle noise from any pixel based on the relative similarities of its scattering properties with each pixel lying in its neighborhood. The despeckling of pixels lying within predefined neighborhood is conducted using a set of 125 new fuzzy inference (FI) rules designed for this purpose. The proposed FL based adaptive weight assignment strategy enhances the efficiency of the designed despeckling technique as it enables it to perform effective speckle noise removal as well as preservation of edges. The effectiveness of this filtering strategy over existing well-known de-speckling filters is demonstrated in the Results section using quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Edge Preservation Index (EPI).