This study focuses on enhancing the diagnosis of myocardial infarction (MI) by extracting distinctive features from ECG graph images and applying machine learning techniques. The authors identify and extract 10 distinct features from ECG signal images, which are then used to train various classification algorithms. Among these, the Gradient Boosting Classifier (GBC) shows the highest accuracy at 88.79%, demonstrating the potential of machine learning in accurately identifying MI. The study highlights the clinical significance of this approach, which can help medical professionals make timely and precise diagnoses, potentially improving patient outcomes. The methodology involves preprocessing ECG images, feature extraction, and applying classifiers, with a focus on data-driven and automated analysis. The results are evaluated using metrics such as accuracy, precision, and F1-Score, and the study concludes with a discussion on the potential of this method in cardiology.This study focuses on enhancing the diagnosis of myocardial infarction (MI) by extracting distinctive features from ECG graph images and applying machine learning techniques. The authors identify and extract 10 distinct features from ECG signal images, which are then used to train various classification algorithms. Among these, the Gradient Boosting Classifier (GBC) shows the highest accuracy at 88.79%, demonstrating the potential of machine learning in accurately identifying MI. The study highlights the clinical significance of this approach, which can help medical professionals make timely and precise diagnoses, potentially improving patient outcomes. The methodology involves preprocessing ECG images, feature extraction, and applying classifiers, with a focus on data-driven and automated analysis. The results are evaluated using metrics such as accuracy, precision, and F1-Score, and the study concludes with a discussion on the potential of this method in cardiology.