This study focuses on enhancing myocardial infarction diagnosis through ECG image analysis and machine learning. The research aims to extract distinctive features from ECG graphs, which are crucial for detecting heart attacks due to variations in ECG signal images. These features serve as significant indicators for distinguishing between various cardiac conditions. The study employs diverse machine learning techniques to simplify and expedite the diagnostic process. The authors identified and used 10 distinct features extracted from ECG signal images, which were then applied to various classification algorithms to evaluate their effectiveness in diagnosing heart conditions. The Gradient Boosting Classifier (GBC) achieved an impressive test accuracy rate of 88.79%, demonstrating the potential of machine learning in accurately identifying heart conditions, including myocardial infarction. The findings highlight the clinical importance of using the gradient-boosting classifier and extracted features from ECG signal images to enhance the accuracy and efficiency of heart attack diagnosis. This can aid medical professionals in making timely and precise diagnoses, potentially improving patient outcomes. The study also discusses the limitations of traditional manual examination of ECG waveforms and introduces machine learning techniques to minimize errors and reduce diagnostic time. The research explores various feature extraction methods and emphasizes the importance of accurate feature selection and extraction in cardiology. The study references previous research on ECG signal analysis and highlights the potential of machine learning classifiers in improving the timely and precise identification of cardiac conditions. The authors used a BPL 6108 machine to acquire ECG graphs and a scanner (L2759A) with a resolution of 300 DPI to ensure maximum detail per inch. The images were saved in JPEG format for compatibility with MATLAB software. The study's methodology includes preprocessing, feature extraction, classifier application, and result evaluation using performance metrics such as accuracy, precision, and F1-score. The research contributes to the advancement of ECG signal analysis and holds promise in improving healthcare quality through automated cardiac condition diagnosis.This study focuses on enhancing myocardial infarction diagnosis through ECG image analysis and machine learning. The research aims to extract distinctive features from ECG graphs, which are crucial for detecting heart attacks due to variations in ECG signal images. These features serve as significant indicators for distinguishing between various cardiac conditions. The study employs diverse machine learning techniques to simplify and expedite the diagnostic process. The authors identified and used 10 distinct features extracted from ECG signal images, which were then applied to various classification algorithms to evaluate their effectiveness in diagnosing heart conditions. The Gradient Boosting Classifier (GBC) achieved an impressive test accuracy rate of 88.79%, demonstrating the potential of machine learning in accurately identifying heart conditions, including myocardial infarction. The findings highlight the clinical importance of using the gradient-boosting classifier and extracted features from ECG signal images to enhance the accuracy and efficiency of heart attack diagnosis. This can aid medical professionals in making timely and precise diagnoses, potentially improving patient outcomes. The study also discusses the limitations of traditional manual examination of ECG waveforms and introduces machine learning techniques to minimize errors and reduce diagnostic time. The research explores various feature extraction methods and emphasizes the importance of accurate feature selection and extraction in cardiology. The study references previous research on ECG signal analysis and highlights the potential of machine learning classifiers in improving the timely and precise identification of cardiac conditions. The authors used a BPL 6108 machine to acquire ECG graphs and a scanner (L2759A) with a resolution of 300 DPI to ensure maximum detail per inch. The images were saved in JPEG format for compatibility with MATLAB software. The study's methodology includes preprocessing, feature extraction, classifier application, and result evaluation using performance metrics such as accuracy, precision, and F1-score. The research contributes to the advancement of ECG signal analysis and holds promise in improving healthcare quality through automated cardiac condition diagnosis.