Recent transition in mobile communication standards suggests massive multiple-input multiple-output (MIMO) to be an integral part of the foreseeable future. However, as antenna elements increase to hundreds in the fifth-generation (5G) and beyond, traditional signal processing methods become prone to significant hardware impairments compound from multiple chains, leading to a substantial performance degradation. This paper explores the effectiveness of generative artificial intelligence (AI) techniques in addressing these challenges within massive MIMO systems. For this purpose, the conditional generative adversarial network (CGAN), a special class of generative AI algorithms, is employed to enhance the accuracy of channel state information (CSI) estimation in a hardware-impaired transceiver setup. This problem is treated as an image-denoising task, where the noise is introduced by the hardware impairments and LS estimation error. Through simulations conducted across various antenna array sizes, the potential of generative AI to improve CSI estimation accuracy under hardware impairments is demon-strated. This highlights its capacity to address critical signal processing challenges in the next-generation wireless systems.