Edge detection is a crucial technique in image processing, serving as a foundation for segmenting an image into distinct areas by identifying sharp intensity contrasts. Its applications span various domains, including object recognition, image analysis, and digital art, necessitating precise demarcation of image boundaries. Despite the existence of several edge detection methods, each relies on specific attributes like pixel intensity and texture to delineate image regions. These methods often incorporate a smoothing phase to reduce noise, using filters like Gaussian, mean, or median, which can inadvertently obscure fine details and subtle edges, impacting the accuracy of edge detection. This paper conducts a comprehensive literature review, focusing on the quantitative impact of smoothing techniques, especially Gaussian blur, on the accuracy of edge detection algorithms. Through an extensive search across major academic databases, the study aims to uncover the nuanced effects of smoothing on edge detection performance, juxtaposing the need for noise reduction against the preservation of critical image features. The review culminates in a discussion on the balance between smoothing and edge clarity, highlighting the trade-offs and proposing avenues for future research to enhance edge detection fidelity in image processing applications.