Context <p>Nanoparticles (NPs) exhibit unique physical and chemical properties that defy classical mechanics, owing to their quantum nature. These properties are dictated by size, shape, and structure, rendering NPs indispensable across diverse applications, including catalysis, medical imaging, drug delivery, and energy research. Advanced computational tools have become indispensable in unraveling the intricacies of nanomaterial behavior, driving significant progress in theoretical and computational research. Among these tools, the density functional theory (DFT) has emerged as a powerful method for predicting material properties. In this review study, we delve into key aspects of DFT simulations applied to nanomaterials, including Optimal Geometries, Band Gap and Electronic Properties, Density of States (DOS), Natural Bond Orbitals (NBO), and spectroscopic features (Infrared, Raman Spectra, and UV–Visible Spectra). Despite its successes, DFT faces limitations, particularly concerning semiconductor materials. Researchers strive to enhance its accuracy while maintaining computational efficiency. Balancing generically accurate functionals for specific applications remains an ongoing challenge. As nanomaterial continues to play a significant part in a variety of industries, the progress of DFT is of great interest and exploration.</p> Methods <p>This review discusses DFT-based computational techniques employed for modeling nanomaterials. The calculations are generally done by utilizing generalized gradient approximation (GGA) functionals such as PBE (Perdew–Burke–Ernzerhof), and where necessary, hybrid functionals like B3LYP to enhance band gap accuracy. All calculations are performed using the standard quantum chemistry packages such as VASP, Gaussian, or Quantum ESPRESSO. This combination of methods offers a complete theoretical basis for the study of nanomaterial properties.</p> Graphical Abstract <p></p>