This thesis investigates the development of a robust machine learning pipeline for the early detection of myocardial infarction (MI) using elec- trocardiogram (ECG) data. Recognizing MI promptly is vital for effective treatment, potentially saving lives by reducing the mortality and morbidity associated with this condition. The research leverages the comprehensive PTB-XL dataset, which includes a vast array of clinical ECG recordings annotated by medical professionals, making it an ideal resource for train- ing and validating the proposed neural network models. The methodology encompasses several stages, starting with the preprocessing of ECG data to enhance signal quality and remove noise and artifacts.