Smart grids rely on precise timing synchronization for efficient operation and real-time decision-making, while conventional GPS-based methods face vulnerabilities. Pulsar-inspired timing offers a promising alternative, but extracting reliable timing signals from pulsar data requires advanced denoising techniques due to the complexity and low signal-to-noise ratio of received signals. This paper proposes a deep learning (DL) algorithm—specifically, an autoencoder-for denoising pulsar signals, aiming to simplify signal processing while improving denoising performance. A comprehensive simulation framework is developed, incorporating both mathematical and physical models to generate realistic synthetic data for training. Performance of the DL model is assessed in comparison with conventional methods such as the wavelet transform, using diverse metrics. Experimental results demonstrate that the DL-based approach outperforms conventional methods, highlighting the effectiveness and adaptability of deep learning techniques for pulsar signal denoising and paving the way for practical implementation of pulsar-inspired timing solutions in smart grid synchronization.