In specific medical contexts, prioritizing the detection of complex cardiac conditions surpasses the importance of identifying common heart ailments. Unique insights into diagnostics have the potential to reveal crucial cardiovascular intricacies that may be crucial in real-world medical scenarios. In this research, we introduce an innovative framework called DX-HeartNet, drawing inspiration from deep learning methodologies while upholding the principles of interpretability in medical decision-making [1]. The primary objective of this study is to empirically demonstrate the effectiveness of the DX-HeartNet model in accurately and transparently detecting complex heart diseases. The proposed model employs an intricate architecture that captures intricate patterns within the data, elucidating the factors that contribute to the prediction of the disease. Unlike other machine learning approaches, DX-HeartNet successfully combines intricate features to uncover latent diagnostic attributes, facilitating comprehensive disease detection. The model’s performance is evaluated using diverse datasets, and its diagnostic capabilities are benchmarked against conventional methods. The results underscore the superiority of DX-HeartNet in identifying heart diseases, thereby outshining the prevailing deep-learning techniques in the realm of cardiac health assessment.