Electrocardiogram (ECG) is a vital diagnostic tool for cardiac conditions. However, manual interpretation of ECG images can be challenging and dependent on the expertise of healthcare professionals. Recent advancements in Machine Learning (ML) have shown promise in improving ECG image analysis accuracy. This paper proposes a methodology that utilizes various ML techniques, including Transfer Learning (TL) and Convolutional Neural Networks (CNNs), to identify clinical issues in ECG images.The study focuses on a dataset of ECG images from individuals with different heart conditions, including anomalies like Myocardial Infarction (MI), irregular heartbeats, and previous MIs. Ten distinct TL models and two CNN models were trained and evaluated to analyze and classify these medical conditions. By integrating TL and CNN techniques, the proposed method enhances the performance of the classifier, enabling accurate diagnosis and prompt treatment for patients.The research highlights the superior performance of ConvNeXt models, particularly ConvNeXtTiny, in terms of accuracy. The analysis is based on CNN model evaluation and the confusion matrix. Additionally, the study demonstrates the potential benefits of combining different CNN models, such as InceptionResNetV2, with a Dense layer to improve the accuracy of interpreting ECG images.