The Speech Enhancement and Reconstruction using GANs approach aims to enhance the clarity and overall quality of speech by employing Generative Adversarial Networks. The GANs are a relatively newer breakthrough in Generative AI technology and have shown promising results. Previously other techniques like Spectral Subtraction were devised for speech enhancement where the noise spectrum estimated from the noisy signal is subtracted from the noisy spectrum to estimate the clean speech spectrum. However, this method can lead to over-subtraction in regions of low SNR, resulting in distorted speech quality. Using GANs can potentially minimize the issues with traditional methods due to their superb ability to generate high-quality results out of noisy data. The capabilities of GANs have already proved their power with image generation, however, their applications with audio data are comparatively much less explored. Thus, the study aims at exploring the capabilities of GANs for the same. The goal is to restore the speaker’s style while filling in the missing words by translating speech to text and assessing context. Applications ranging from accessibility technologies to teleconferencing could benefit from this all-encompassing approach.