This research proposes a non-invasive method for predicting blood groups using deep learning, specifically leveraging the EfficientNet architecture. The method aims to provide a swift and accurate determination of blood types, which is crucial in medical emergencies before administering blood transfusions. Traditional methods, such as automated blood analyzers, are time-consuming and can cause patient discomfort due to skin pricking. The proposed approach uses a laser light to illuminate the finger, capturing images of superficial blood vessels that reveal specific antigen shapes like 'A' and 'B' antigens. These images are then analyzed by a deep learning model to predict the blood group. The system requires a high-definition camera to capture the antigen patterns from the red blood cells' surface, eliminating the need for skin piercing. The proposed solution is cost-effective, easy to implement, and offers significant advantages in terms of immediate identification of ABO blood groups, making it particularly valuable in medical emergencies, military scenarios, and for infants where invasive procedures pose additional risks. The research includes a detailed methodology for data collection, statistical analysis, and the implementation of the EfficientNet model. The dataset consists of 103 samples from different blood groups, with a focus on balancing the distribution to avoid overfitting. The EfficientNet model was trained with 30 epochs and evaluated on training and validation datasets, showing an 84.54% training accuracy and a 55.56% validation accuracy. The model outperformed other CNN models like ResNet and VGG-16 in terms of validation accuracy and loss, despite having a shorter training time. The study concludes that the proposed non-invasive blood group prediction system holds potential for addressing the need for affordable and efficient blood group determination, especially in resource-constrained settings. Future work could involve exploring other deep learning architectures, expanding the dataset, and incorporating data augmentation techniques to further enhance the system's accuracy and applicability.This research proposes a non-invasive method for predicting blood groups using deep learning, specifically leveraging the EfficientNet architecture. The method aims to provide a swift and accurate determination of blood types, which is crucial in medical emergencies before administering blood transfusions. Traditional methods, such as automated blood analyzers, are time-consuming and can cause patient discomfort due to skin pricking. The proposed approach uses a laser light to illuminate the finger, capturing images of superficial blood vessels that reveal specific antigen shapes like 'A' and 'B' antigens. These images are then analyzed by a deep learning model to predict the blood group. The system requires a high-definition camera to capture the antigen patterns from the red blood cells' surface, eliminating the need for skin piercing. The proposed solution is cost-effective, easy to implement, and offers significant advantages in terms of immediate identification of ABO blood groups, making it particularly valuable in medical emergencies, military scenarios, and for infants where invasive procedures pose additional risks. The research includes a detailed methodology for data collection, statistical analysis, and the implementation of the EfficientNet model. The dataset consists of 103 samples from different blood groups, with a focus on balancing the distribution to avoid overfitting. The EfficientNet model was trained with 30 epochs and evaluated on training and validation datasets, showing an 84.54% training accuracy and a 55.56% validation accuracy. The model outperformed other CNN models like ResNet and VGG-16 in terms of validation accuracy and loss, despite having a shorter training time. The study concludes that the proposed non-invasive blood group prediction system holds potential for addressing the need for affordable and efficient blood group determination, especially in resource-constrained settings. Future work could involve exploring other deep learning architectures, expanding the dataset, and incorporating data augmentation techniques to further enhance the system's accuracy and applicability.