Through the analysis of electrocardiogram (ECG) images, this study explores the application of machine learning and deep learning techniques for the early identification of cardiovascular illnesses. We evaluate the efficacy of conventional algorithms and cutting-edge deep learning techniques using a variety of datasets, preprocessing techniques, and feature extraction approaches model as the LSTM. While robustness is ensured by validation on separate datasets, the application of transfer learning improves model generalization. A focus on interpretability seeks to promote clinical acceptance, which is essential for incorporating new techniques into standard medical procedures. Overall, this study shows how these methods have the potential to transform the diagnosis of cardiovascular disease, allowing for prompt interventions and ultimately leading to better patient outcomes. Additionally, by permitting preemptive therapies, this interdisciplinary approach holds promise for revolutionizing cardiovascular healthcare.