The signal under consideration is a biologically significant indicator utilized to diagnose heart disorders, as it exhibits the cyclic contraction and relaxation patterns of the myocardial muscles in the human heart. This noninvasive tool is crucial in identifying life-threatening heart conditions, such as abnormal electrocardiogram (ECG) heartbeats and arrhythmias, which can lead to death. Premature ventricular contraction (PVC), one of the most prevalent arrhythmias, arises from the ventricular region of the heart and has the potential to induce palpitations, cardiac arrest, and various other symptoms that may impede a patient’s daily functioning. To reduce doctors’ workload in assessing heart arrhythmia and disease, computer-assisted techniques are now used to diagnose them automatically. This study proposes a machine learning (ML) methodology for the identification of PVCs using the MIT-BIH arrhythmia database. The feature extraction method entails the computation of ten distinct features related to heartbeats. Three morphological aspects, including R wave amplitude, QRS complex duration, and QRS complex shape, were examined. Additionally, seven statistical features were calculated for each signal. These features were derived from 8 s of ECG data, resulting in a feature vector. A support vector machines (SVM) model was utilized to analyze these features, identify distinct patterns, and improve classification accuracy. The outcomes highlight the substantial enhancement in diagnostic performance.