Cardiac MRI images are vital in diagnosing a range of heart diseases, yet standard solutions frequently struggle with inadequate region delineation, confusion among similar pathologies, and opaque decision-making processes. In this work, we aim to resolve these problems by introducing dedicated methods for careful region extraction, specialized classification, and metric-based interpretation. Our approach notably improves segmentation, achieving Dice coefficients of 0.974 for the left ventricle and 0.947 for the right ventricle—outperforming prior baselines. Classification results reach a 97% overall accuracy, substantially higher than reference architectures that only attained 72–84%. Furthermore, clinical relevance is enhanced through a structured output that pinpoints key anatomical and functional indicators. These findings suggest a reliable pipeline that refines MRI analysis and facilitates healthcare professionals making more informed decisions in real-world medical settings.