Cardiac arrhythmias pose a significant challenge in clinical diagnostics, necessitating accurate and efficient detection methods. This study explores the classification of arrhythmias using advanced machine learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). A dataset of 16,000 simulated ECG signals, generated using SimVascular, provided the foundation for training and evaluation. CNNs achieved high accuracy in spatial feature extraction, while LSTMs excelled in capturing temporal dependencies in sequential ECG data. PINNs emerged as the most robust model, achieving a training accuracy of 97.8% and a testing accuracy of 97.2%, leveraging domain-specific constraints from the FitzHugh-Nagumo equations. The results highlight the complementary strengths of these models, with PINNs offering superior interpretability and physiological consistency. Future work will focus on integrating multi-modal data and developing real-time systems to advance arrhythmia diagnostics and improve cardiac care outcomes.