The objective of this research is to employ machine learning techniques for the accurate diagnosis of cardiac diseases by leveraging a combination of diverse features. This study introduces an automated methodology that involves the analysis of non-stationary heart sound signals to effectively identify various heart conditions. In contrast to conventional approaches relying on techniques such as analysis of variance, signal average or standard deviation comparison, the proposed diagnosis is primarily based on diagnostic labels, ensuring a more reliable and robust assessment. The integrated system proposed in this study utilizes the renowned MIT-BIH database to analyze heartbeats and discern different functional states. Training and testing of the data are performed using the K-fold cross-validation procedure, and a novel model is employed to enhance the learning process. Through this innovative diagnostic system, the detection of cardiac abnormalities in electrocardiogram (ECG) signals is achieved with an impressive accuracy ranging from 96% to 99% across a broad spectrum of cases. By harnessing the power of machine learning algorithms and leveraging a comprehensive set of features, this research significantly advances the field of cardiac disease diagnosis. The proposed methodology outperforms traditional approaches by providing a more accurate and efficient means of identifying heart conditions. The utilization of diagnostic labels as the basis for diagnosis ensures enhanced reliability, enabling healthcare professionals to make informed decisions regarding patient care. Ultimately, this research contributes to the ongoing efforts to improve cardiac healthcare, enabling early detection and intervention, and potentially saving numerous lives. Manuscript profile