Atrial Fibrillation (AF) is a prevalent form of arrhythmia that primarily affects the atria (upper chambers of the heart), causing disorganized electrical signals and resulting in an irregular heartbeat. Electrocardiography (ECG) is the most commonly used technology for detecting AF, as ECG signals provide crucial information on cardiac irregularities, making it an essential tool for AF diagnosis. However, manually analyzing ECG signals is both mentally demanding and time-consuming. In this study, we propose Hybrid CNN-LSTM model to determine whether a given ECG signal represents a normal sinus rhythm or indicates Atrial Fibrillation. We utilize the Physionet Database for our dataset. The proposed hybrid model aims to assist doctors by significantly reducing the time and mental effort required for diagnosis, facilitating early detection of the disease and lowering the risk of thromboembolic events, including ischemic strokes. Our Hybrid CNN-LSTM model is designed to autonomously learn discriminative features from raw ECG signals, eliminating the need for manual feature extraction. We compared the performance of the Hybrid CNN-LSTM model with a standard CNN model. The Hybrid CNN-LSTM model achieved an accuracy of 95.2%, outperforming the CNN model, which achieved an accuracy of 81.4%. Therefore, our proposed model offers a more accurate method for