This study evaluates the accuracy and reliability of brain hemorrhage prediction using the EfficientNet B7 model. The model achieved an accuracy of 94.2% and a recall of 94.0%, demonstrating high sensitivity that enhances its clinical applicability. The model achieved a loss of 0.40 during training and validation, showing stable convergence. These results expand the potential for AI in medical image analysis, ultimately contributing to improved diagnostic accuracy for healthcare professionals. Future research will verify the model's versatility using diverse datasets and increase interpretability for better clinical integration.