In the field of cardiovascular disease diagnosis, electrocardiogram (ECG) signals are used to identify conditions like arrhythmia and myocardial infarction (MI). These signals reflect the electrical activity of the heart to help cardiologists diagnose cardiac disorders. However, due to the limited amplitude and duration of the ECG signals, visual interpretation is challenging. Therefore, machine learning models have become valuable for automatically detecting MI using ECG data. In this paper, we aim to develop an automatic technique for classifying MI using Convolutional Neural Networks (CNNs) and Gated Recurrent Unit (GRU) networks. The data is first processed to extract heartbeats segments, which are then used to train the CNN and GRU models to recognize MI patterns. Our trained models are applied to the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database, achieving an average accuracy of 75% and a sensitivity of 80% for MI diagnosis. This demonstrates the potential of using deep learning techniques to improve the diagnosis of cardiovascular diseases