Electrocardiogram (ECG) signal analysis plays a vital role in the diagnosis and monitoring of cardiac diseases. Traditional methods rely heavily on manual interpretation, which can be time-consuming and prone to human error. This research explores the application of deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, for automated classification of ECG signals. Using a publicly available ECG dataset, the proposed model undergoes thorough preprocessing, feature extraction, and training to accurately distinguish between different cardiac conditions. The experimental results demonstrate that LSTM networks achieve high accuracy and robustness in ECG classification tasks, outperforming traditional machine learning approaches. This study highlights the potential of deep learning models to enhance automated cardiac diagnostics, with implications for real-time healthcare monitoring systems. Future work suggests further optimization and validation across diverse datasets to improve clinical applicability.