High-frequency oscillations (HFOs) are vital biomarkers for identifying the seizure onset zone (SOZ) in patients with drug-resistant epilepsy (DRE). However, EEG artifacts especially muscle and power-line noise overlapping with the HFO frequency range (80–250 Hz) pose significant challenges for accurate detection. Traditional artifact removal methods like independent component analysis (ICA) are labor-intensive and subjective, highlighting the need for automated pre-processing techniques. This study introduces CleanEEG, a U-Net based encoder–decoder model designed to automate artifact removal from clinical EEG. CleanEEG was trained on paired noisy and clean sleep EEG segments from 25 DRE patients (177 segment pairs) at a 512 Hz sampling rate, with clean targets generated through ICA pre-processing. Model performance was quantitatively evaluated on an independent validation set comprising 24 segment pairs from six separate patients excluded from training. Evaluation metrics included relative root mean square error (RRMSE), correlation coefficient (CC), and signal-to-noise ratio (SNR). CleanEEG effectively removed muscle and power-line noise artifacts while preserving important clinical features such as interictal epileptiform discharges (IEDs) and brief potentially ictal rhythmic discharges (BIRDs). The model significantly improved signal quality across electrodes, reducing reconstruction errors and increasing SNR. Additionally, CleanEEG preserved neural activity without introducing distortions and qualitatively demonstrated artifact removal capability on unseen awake EEG data. In a representative DRE patient, critical spatial patterns of HFOs were maintained, essential for accurate SOZ localization. Overall, CleanEEG offers an automated, robust, and efficient solution for artifact removal, enhancing diagnostic accuracy in epilepsy monitoring and HFO analysis, particularly in long-term scalp EEG recordings.