Electrocardiogram (ECG) analysis is crucial for the identification and classification of cardiac arrhythmias; it is a gold standard for cardiovascular mortality. In this work, a well-structured classification process for identifying cardiac arrhythmias using the MIT-BIH dataset. This dataset consists of a raw ECG signal pre-processed with wavelet transform to remove noise without affecting diagnostic features. The PanTompkins algorithm identified R-peaks of the segmented ECG signal. Annotations of heartbeat are divided into five classes: normal, ventricular ectopy, supraventricular ectopy, fusion, and unclassified beats, according to the Association for the Advancement of Medical Instrumentation (AAMI EC57) standards. The imbalance in classes is balanced using a hybrid method of Synthetic Minority Over-sampling Technique (SMOTE) and random undersampling. The balanced classes were trained and tested using machine learning models to perform the detection of arrhythmias that enhance the accuracy, precision, and recall. The Random Forest classifier is an ensemble method performs the classification of arrhythmia with an accuracy of 99.56%.