Low-light image enhancement (LLIE) is a critical component in computer vision pipelines, particularly in domains such as surveillance, autonomous driving, medical imaging, and photography. Images captured under poor illumination often suffer from low brightness, noise, and color distortions, which degrade the performance of downstream vision tasks. This project proposes AMGAN — an Attention-Driven Multi-Scale GAN integrated with a Complementary Learning Sub-Network (CLSN) — to address the complex challenges of LLIE, particularly under non-uniform lighting conditions. The CLSN generates an inverse grey map that acts as an adaptive attention map, selectively enhancing underexposed regions while preserving well-lit areas. This attention-guided map is concatenated with the original low-light image to form an intermediate enhancement. The GAN’s generator, based on a U-Net architecture, extracts both spatial and frequency-domain features using Fast Fourier Transform (FFT), enabling fine-grained texture preservation and natural color consistency. A dual Markov discriminator ensures that both global and local image realism is maintained. The model is trained and evaluated on benchmark datasets such as LSRW, LOLv1, LOLv2-real, and LOLv2-synthetic, achieving competitive performance in terms of PSNR, SSIM, and perceptual quality. Experimental results show that AMGAN balances enhancement quality with computational efficiency, making it a viable solution for real-world LLIE applications. Future work will explore architectural optimizations and potential domain-specific extensions.