In recent years, deep learning techniques have revolutionized the field of medical imaging and diagnostics, offering new avenues for the early detection and accurate classification of various diseases. Myocarditis, an inflammation of the heart muscles, is a critical condition that requires timely diagnosis to initiate appropriate treatment and prevent potential complications. This chapter presents a comprehensive exploration of a deep learning approach to detect myocarditis using medical imaging data. The chapter begins by discussing the overview of myocarditis, including its causal symptoms and potential consequences if left untreated. The treatment of these critical diseases has been tackled using deep learning techniques in the past and it continues to grow. The next sections focus on the use of deep learning techniques to classify heart images as normal or abnormal. The reader is introduced to the convolutional neural networks and transfer learning techniques followed by their application on the myocarditis dataset. A comparative and experimental study of different pretrained models along with feature extraction and a highlight of their strengths and limitations is presented in the further sections illustrated along with codes and experimental results for the better understanding of the readers. The chapter concludes by discussing the challenges and future directions in the application of deep learning for myocarditis detection and how it deeply benefits society at large.