Malaria continues to affect human lives extensively around the world, requiring urgent medical diagnostic procedures. This paper presents an improved version of the U-Net deep learning method, which identifies malaria within microscopic blood smear images. The segmentation-based feature extraction within U-Net offers superior performance when compared to ordinary deep learning methods, thus leading to better detection results. U-Net delivers precise location detection of diseased areas, which boosts accuracy, while CNN focuses on identification categories, and ANN faces difficulties when identifying complex spatial patterns. The experimental outcomes indicate that U-Net surpasses ANN and CNN approaches by delivering higher values for sensitivity and specificity. The model provides exact detection results and avoids human mistakes while shortening diagnostic time. The system is suited for practical deployment besides offering optimal performance when resources are limited. Data augmentation techniques improve overall generalization properties, which makes the system resistant to different datasets. A modern technological system for automated malaria detection uses deep learning as its foundation.