Cardiovascular diseases (CVDs) are regarded as one of the leading causes of mortality worldwide, making the early detection of arrhythmias crucial for minimizing their impact. Electrocardiogram (ECG) signals are widely used to monitor heart health, and the automation of arrhythmia classification is deemed essential for enhancing diagnostic accuracy in clinical settings. In this study, a comprehensive framework for automated arrhythmia detection is presented, featuring an advanced preprocessing pipeline that combines band-pass filtering, notch filtering, and wavelet transform to eliminate noise from raw ECG signals effectively. Following noise removal, the R-peaks of the signals are detected, and based on these R-peaks, optimized window settings are applied for heartbeat segmentation, ensuring high-quality data for further analysis. The processed heartbeats undergo classification through several deep learning architectures: convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and a hybrid CNN+BiLSTM model. Model performance is optimized through hyperparameter tuning using grid search and 5-fold cross-validation. Ultimately, exceptional results are achieved by the proposed CNN+BiLSTM model, with an accuracy of 99.435%, precision of 99.434%, recall of 99.436%, and an F1 score of 99.434%. These findings emphasize the potential of this approach as a reliable clinical tool for arrhythmia detection, particularly in healthcare environments.