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The rapid and precise identification of blood groups is critical in the practice of safe transfusion. In this work, the researcher describes an automated blood group detector with an image processing and the YOLOv8 object detector algorithm. The system identifies the blood groups (A, B, AB, O) and Rh factors through the identification of agglutination patterns on the images of blood test cards. The techniques of preprocessing such as image resizing, Gaussian noise reduction, histogram equalization, and transformation of color space are used to enhance the clarity of the features under different conditions. Model robustness is also increased by data augmentation. The trained YOLOv8 model is highly accurate in real time and is a viable and reliable substitute of manual blood group detection, particularly in areas with limited resources.