Three domains are available for the analysis of electroencephalogram (EEG) signals: spatial, temporal, and frequency. These signals are tainted by noise and distortions during the information collecting stage, making assessment more challenging. Human intervention is necessary for the removal of noise and artifacts from techniques like independent component analysis (ICA). Using a reduced dimensional latent space to represent inputs, autoencoders have automated the identification and elimination of artifacts. The existing approaches to EEG denoising involve the use of decomposition, thresholding, and filtering techniques. EEG signals play a crucial role in understanding brain activity patterns, aiding in medical diagnosis, and facilitating brain–computer interface applications. However, EEG signals are often contaminated by various artifacts and noise, hindering their accurate analysis. This study proposes a novel approach to enhance EEG signals by combining a Faster Mask Recurrent Convolutional Neural Network (FMRCNN) with denoising autoencoders. The proposed method leverages the capabilities of FMRCNN to efficiently capture temporal dependencies and spatial features from EEG data. By incorporating mask mechanisms, the network focuses on relevant information while suppressing noise and artifacts. Furthermore, denoising autoencoders are employed to further refine the EEG signals by learning robust representations and removing unwanted noise components. A great deal of investigations is carried out utilizing publicly available EEG datasets in order to assess the efficacy of the suggested technique. Comparative analyses with existing methods demonstrate superior performance in denoising EEG signals and preserving relevant brain activity patterns. Furthermore, the suggested approach demonstrates improved computing effectiveness, which qualifies it for real-time applications. Overall, the integration of FMRCNN and denoising autoencoders presents a promising avenue for improving the quality of EEG signals, which can lead to more accurate brain activity analysis and better clinical outcomes in neuroscience research and medical diagnostics.