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Rapid and precise blood group identification plays a vital role in emergency healthcare scenarios. Traditional methods rely on invasive blood testing procedures, which are time-consuming and resource-dependent. This paper presents a non-invasive, AI-powered technique for blood group prediction using fingerprint images. A Convolutional Neural Network (CNN) model is designed to classify biometric fingerprint patterns into eight major blood groups. Developed using PyTorch and integrated with a Streamlit web interface, the proposed system provides real-time, contactless predictions with confidence scores. The model demonstrates the potential of deep learning in biometric-health correlation and lays the foundation for non-invasive diagnostics in clinical settings.