Efficient analysis of blood samples is pivotal in the medical domain, particularly in identifying abnormalities within blood cells that are often indicative of various health issues. Red blood cells (RBCs) constitute a significant component of blood, and their classification is instrumental in diagnosing a spectrum of diseases. The conventional method of manually visualizing RBCs under a microscope is not only time-consuming but also prone to human errors, potentially leading to misinterpretations. Pathological conditions can alter the shape, texture, and size of normal RBCs, making an automated and accurate classification method crucial. By extracting features from segmented cell images, the algorithm categorizes RBCs into distinct types, including Microcytes, Elliptocytes, Stomatocytes, Macrocytes, Teardrop RBCs, Codocytes, Spherocytes, Sickle cell RBCs, and Howell-Jolly RBCs. The classification is based on the size, shape, and overall appearance of RBCs. To validate the proposed method, experiments were conducted using blood slides collected from a hospital, and RBC images were extracted from these slides. The obtained results were compared with reports from the pathology lab, revealing an impressive accuracy of 98.5%. The developed system not only ensures accuracy but also expedites the process, potentially contributing to saving lives by providing swift and reliable results. This automated approach represents a significant advancement in the field of medical diagnostics, offering a reliable and efficient means of RBC classification for improved patient outcomes.