Cardiac Ailments are the most widespread diseases worldwide that affect the heart’s functioning. Automatic detection of these diseases plays a vital role in immediate diagnosis. Many state-of-the-art algorithms were proposed for automatically classifying cardiac diseases involving hand-crafted feature extraction. This work used several classifiers for detecting heart ailments using Electrocardiogram (ECG) data to classify four cardiac ailments, i.e., Arrhythmias, Congestive Heart Failure, Atrial Fibrillation, and Normal Sinus Rhythm. The ECG signals were considered for the MIT-BIH physio-net Database. Gradient Boosting (GB) emerged as the best-performing classifier, scoring at the greatest possible level (100%) in precision, recall, F1 score, ROC AUC, and accuracy, and the outstanding performance demonstrates GB’s better capacity for correctly diagnosing cardiac conditions using ECG data. Nevertheless, the accuracy ratings of 99.21%, 96.89%, and 98.23% obtained by Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), respectively, also demonstrated good categorization. LR, SVM, and KNN all had accuracy scores much lower than GB’s, but these findings show how effective LR is at spotting cardiac problems. The results provide useful information regarding the classifier’s effectiveness and can assist in developing systems that can accurately identify heart conditions using ECG data.