Tunnel magnetoresistance (TMR) sensors have been recognized as a cost-effective alternative for measuring magnetocardiography (MCG) signals. However, their relatively high noise levels and susceptibility to contamination limit their practical clinical applications. To address these challenges, we propose a novel Multi-Level Gated U-Net (MGU-Net) model specifically designed for denoising long sequential MCG signals obtained from TMR sensors. The MGU-Net leverages the U-Net architecture to learn hierarchical representations, integrated with a novel Gated Linear Unit (GLU) module to capture the periodic pattern of Q, R, and S wave complex (QRS complex) from MCG. This design enhances periodic cardiac signatures and suppresses irregular noise components through adaptive gating mechanisms. We have developed a TMR-based MCG system and collected both simulated and real MCG data in a magnetically shielded environment. The results show that our method improve signal-to-noise ratio (SNR) from −2.142 dB to 10.505 dB on the simulated MCG dataset and from 3.958 dB to 14.514 dB on the real dataset, surpassing other state-of-the-art methods. Our model successfully recovers subtle P-wave and T-wave features from the noisy signals, illustrating a promising direction of using TMR-based systems for potential practical clinical applications.