Recent advances in image processing have primarily focused on improving image quality when affected by environmental conditions such as rain. This paper introduces TOCyG-Net, a novel architecture that uses Total Generalized Variation (TGV), Cycle-Consistent Generative Adversarial Networks (CycleGAN), and Orthogonalized Iterative Shrinkage (OIS) to effectively remove rain streaks from images. The proposed method starts with TGV for initial image smoothing, which reduces noise while preserving important structural features like edges and texture. CycleGAN then facilitates the transition from a rain-impaired to a rain-free image state by learning the mapping between these domains. Finally, the OIS method refines the image further by reducing less significant coefficients, which improves overall image clarity and detail retention. TOCyG-Net outperforms existing models, with a Peak Signal-to-Noise Ratio (PSNR) of 41.26 and a Structural Similarity Index (SSIM) of 0.994, indicating superior detail preservation and noise reduction performance. This approach not only sets a new standard for rain streak removal, but it also paves the way for real-time image processing applications by addressing some of the common limitations of current techniques, such as high computational demands and training stability issues.