Accurate diagnosis and therapy planning in cardiovascular disorders depends on automated left ventricle (LV) segmentation using cardiac magnetic resonance imaging (MRI). The research outlines an effective automatic left ventricle segmentation system based on the U-Net architecture which runs on MATLAB platforms. A chosen dataset containing MRI left ventricle images and their corresponding ground truth segmentations forms the starting point of this procedure. The advanced semantic segmentation network U-Net receives optimized training through extensive hyperparameter optimization and data augmentation methods applied to this specified dataset. The approach enables precise left ventricle segmentation while maintaining total processing speed suitable for clinical use. The proposed approach resolves LV segmentation challenges through its implementation of robust U-Net architecture and MATLAB image processing tools to yield precise and dependable research findings. Research results show that deep learning algorithms possess vast potential for automated medical image processing which will advance non-invasive cardiovascular testing.