Heart arrhythmia is considered a global health issue that requires early detection and contributes to the complexity of diagnosis and treatment. This paper introduces a deep learning approach that uses CNNs and LSTM networks to classify arrhythmias with high accuracy. Robust analysis is ensured by automating important procedures like signal preprocessing, feature extraction, and temporal pattern detection using combined CNNs for spatial feature extraction and LSTMs for temporal dependence tracking. Conventional approaches to arrhythmia detection involve physicians manually interpreting ECG readings, which is time-consuming and error-prone. With the advent of deep learning and AI, these limitations can be bypassed by automating the diagnosis process. While LSTMs capture the dynamic temporal patterns required for arrhythmia detection, CNNs are highly effective at detecting anomalies in waveform structures. Our model, trained on the MIT-BIH Arrhythmia dataset, achieved an accuracy of 95%, significantly outperforming traditional methods by 77%, with a precision of 91%, recall of 90%, and an F1-score of 90.5%. The primary reason for this initiative is the need for faster, more precise, and scalable diagnostic tools in heart health. Compared to conventional methods, modern AI techniques diagnose patients much more rapidly and accurately, thereby enhancing patient care and reducing the chances of potentially fatal outcomes such as stroke and sudden cardiac arrest. This method may thus revolutionize the identification and treatment of cardiac arrhythmias through AI-powered tools, providing a robust and scalable solution for real-world clinical applications.