Generative AI with spectroscopy is an innovative approach that combines artificial intelligence techniques with spectroscopic analysis, enabling researchers to extract valuable insights from complex spectral data more efficiently and accurately. AI can create high-precision synthetic spectra, enhance signal-to-noise ratios, and support spectral reconstruction by employing deep learning techniques like Generative Adversarial Networks (GANs), Graph Neural Networks (GNN), and Variational Autoencoders (VAEs). GenAI models facilitate the simulation of spectra from molecular structures, solving the forward design, whereas reasoning-driven models address the inverse design by predicting molecular structures with greater accuracy. GenAI can be integrated with spectroscopy to achieve various tasks such as improvement of spectral data analysis, rapid advancement in material discovery, automation, closed-loop optimization, etc. These innovations allow faster and more precise material identification, and real-time spectral prediction across diverse fields such as pharmaceuticals, environmental monitoring, and materials science. Despite challenges related to data quality, model interpretability, and computational demands, integrating Generative AI with spectroscopy offers significant potential for driving advancements in scientific research and industrial applications.