Identifying an individual’s blood group is a critical requirement in medical emergencies, forensic investigations, and healthcare systems; however, conventional methods rely on invasive procedures and laboratory testing. This study proposes a novel, non-invasive approach to predict blood groups using fingerprint map analysis. The motivation behind this research lies in exploring the correlation between fingerprint patterns and blood group types to develop a faster and more accessible identification system. The proposed method utilizes fingerprint image acquisition followed by preprocessing, feature extraction, and classification techniques to analyze ridge patterns such as loops, whorls, and arches. Machine learning algorithms are applied to map extracted fingerprint features with corresponding blood group categories, enabling automated prediction. A dataset consisting of fingerprint samples along with known blood group information is used to train and evaluate the model. The experimental results demonstrate that the system can achieve promising accuracy in predicting blood groups, highlighting the feasibility of fingerprint-based identification. This approach reduces the need for invasive testing and provides a rapid alternative for preliminary blood group estimation. The findings suggest that the proposed system can be effectively applied in healthcare support systems, biometric identification, and emergency response scenarios. Overall, this research contributes to the development of innovative, non-invasive biometric techniques for medical and forensic applications.