This research proposes a non-invasive blood group prediction system using an optimized EfficientNet deep learning architecture. The system aims to determine blood types without invasive procedures, which is critical in medical emergencies where rapid and accurate blood group identification is essential for safe transfusions. Traditional methods rely on time-consuming automated blood analyzers and involve skin pricking, which can cause bleeding and discomfort. The proposed system uses laser light to illuminate the finger, capturing optical images of superficial blood vessels that contain antigen patterns. These patterns are analyzed to predict blood groups. The system uses a high-definition camera to capture antigen patterns from red blood cells without piercing the skin, offering a cost-effective and efficient solution. The system is based on a deep learning model that processes images of blood vessels on the finger. The model is optimized for performance and accuracy, and its effectiveness is compared with other CNN models such as ResNet and VGG-16. The results show that the optimized EfficientNet model achieves a validation accuracy of 55.56%, outperforming the other models. The system is designed to be compact, cost-effective, and customizable, making it suitable for use in various settings, including medical emergencies, military scenarios, and healthcare in developing countries where invasive procedures are not feasible. The research highlights the importance of non-invasive blood group prediction in improving healthcare outcomes and reducing the risks associated with traditional methods. The proposed system offers a reliable and efficient solution for blood group determination, leveraging deep learning to analyze optical images of blood vessels. The study also discusses the challenges of working with small datasets and the need for data augmentation and advanced machine learning techniques to improve model performance. The system's ability to accurately predict blood groups has significant implications for medical applications, particularly in situations where rapid and accurate blood group identification is crucial.This research proposes a non-invasive blood group prediction system using an optimized EfficientNet deep learning architecture. The system aims to determine blood types without invasive procedures, which is critical in medical emergencies where rapid and accurate blood group identification is essential for safe transfusions. Traditional methods rely on time-consuming automated blood analyzers and involve skin pricking, which can cause bleeding and discomfort. The proposed system uses laser light to illuminate the finger, capturing optical images of superficial blood vessels that contain antigen patterns. These patterns are analyzed to predict blood groups. The system uses a high-definition camera to capture antigen patterns from red blood cells without piercing the skin, offering a cost-effective and efficient solution. The system is based on a deep learning model that processes images of blood vessels on the finger. The model is optimized for performance and accuracy, and its effectiveness is compared with other CNN models such as ResNet and VGG-16. The results show that the optimized EfficientNet model achieves a validation accuracy of 55.56%, outperforming the other models. The system is designed to be compact, cost-effective, and customizable, making it suitable for use in various settings, including medical emergencies, military scenarios, and healthcare in developing countries where invasive procedures are not feasible. The research highlights the importance of non-invasive blood group prediction in improving healthcare outcomes and reducing the risks associated with traditional methods. The proposed system offers a reliable and efficient solution for blood group determination, leveraging deep learning to analyze optical images of blood vessels. The study also discusses the challenges of working with small datasets and the need for data augmentation and advanced machine learning techniques to improve model performance. The system's ability to accurately predict blood groups has significant implications for medical applications, particularly in situations where rapid and accurate blood group identification is crucial.