Interpreting spectral data through biomolecular spectroscopy, such as nuclear magnetic resonance, infrared, and UV–Vis spectra, is crucial for determining the molecular structure. Advancements in computer science and artificial intelligence have made it easier to analyze vast amounts of data, particularly in cheminformatics. Intelligent expert systems rely on predetermined problem-solving strategies, which are derived from human experts and converted into algorithms. Artificial intelligence (AI) holds enormous potential in addressing these issues and the emergence of AI with computer technology strengthens scientists as well as has a large influence on society by rapidly generating new drugs candidates. To predict entire absorption spectra through cellular and biomolecular spectroscopy, huge datasets of consistent spectra are required to anticipate several continuous variables, such as molar extinction coefficients at various wavelengths. Based on AI algorithms, organic compounds are identified based on their absorption spectrum using molecular descriptors. The results obtained from biomolecular spectroscopy based on AI methods can also help to evaluate photo safety by classifying chemicals based on their absorption spectral data. Predicting biomolecular spectra from chemical structures through AI-based techniques is important for designing novel materials, identifying phototoxic compounds, estimating missing spectroscopic data and moderating experimental spectra databases. This chapter addresses current advancements and recent breakthroughs in AI-based spectrum interpretation through cellular and biomolecular spectroscopy, which involves extracting usable information from spectrum to understand complex structures, analyzing spectral information, and processing of data. This chapter also provides our own perspectives and recommendations on the development, potential uses, and implementation of AI-based spectral interpretation through cellular and biomolecular spectroscopy.