Image hashing algorithms play a pivotal role in the field of computer vision, encompassing applications such as digital image processing, image retrieval, content identification, verification, and duplication detection across diverse digital platforms. This study presents a quantitative comparative analysis of four widely recognized and fundamental image hashing algorithms including Average Hash (A-Hash), Difference Hash (D-Hash), Perceptual Hash (P-Hash), and BlockHash. The study evaluates the accuracy and robustness of each algorithm in identifying transformed images and assesses them across seven common image transformations such as brightness adjustment, resizing, contrast modification, sharpening, blurring, color adjustment, and rotation. The key result indicates that while A-Hash maintains a higher similarity percentage in many cases, P-Hash and BlockHash offer more detailed and reliable comparisons for complex transformations. The Hamming distance is employed to determine the accuracy rate of each algorithm across these transformations. This study provides valuable insights into the balance between robustness and sensitivity for each hashing algorithm when applied to day-to-day image transformations.