Multiple sclerosis (MS) is a chronic autoimmune disease characterized by inflammatory brain lesions, making MRI-based lesion segmentation challenging due to noise, missing data, and limited availability of high-quality labeled images. This paper presents RAUM-GANs, a multi-layer deep learning framework designed to address these challenges and enhance segmentation accuracy. The preprocessing stage comprises three layers: (1) noise reduction using a modified Denoising GAN (DGAN-Net), achieving peak signal-to-noise ratio (PSNR) values up to 42.21 dB across varying noise levels; (2) missing data imputation through advanced GAN-based methods, ensuring clinically reliable reconstruction of incomplete MRI scans; and (3) dataset expansion via a Multi-level Identity GAN (MGAN), which incorporates an identity block to prevent mode collapse, an 8-connected pixel constraint to maintain spatial coherence, and a softened discriminator output to mitigate vanishing gradients. For segmentation, a Residual Attention U-Net (RAU-Net) with identity mapping is employed, yielding precise detection and delineation of MS lesions. Extensive evaluation on the MICCAI MSSEG-2 dataset demonstrates that RAUM-GANs outperform four state-of-the-art methods, achieving a Dice score of 96.6%, Fréchet Inception Distance (FID) of 43.13, and Inception Score (IS) of 14.03. The results highlight the framework’s ability to generate high-quality synthetic MRI data, improve robustness against noise and incomplete information, and deliver superior lesion segmentation performance. RAUM-GANs provides a comprehensive, scalable solution for MS lesion analysis, with potential applicability to other medical imaging domains where data quality and scarcity remain significant barriers.