We investigate the infuence of spin and impurity on the density of states of SiC nanotubes employing Density Functional
Theory (DFT) and a Machine Learning (ML) based framework. Our study investigates the electronic structures and magnetic properties of various SiC nanotube confgurations, including wurtzite, Co-doped, and undoped single-wall (6,0) chiral nanotubes, employing both DFT and pseudopotential approaches. The calculated energy band gap values for SiC bulk structures, nanotubes, and doped systems, retaining local density and local spin density approximations with the Hubbard U method, exhibit distinct characteristics. While undoped SiC systems remain nonmagnetic whereas Co-doped SiC systems show magnetic properties, with a total magnetic moment of around ~ 1.9 µB. Our frst-principles calculations indicate that Co-doped SiC nanotubes induce magnetism, however the total energy calculations revealed satisfactory stability for the ferromagnetic phase. Validation against DFT data demonstrates that our model achieves approximately 91.5% accuracy for predicting the density of states for quantum-confned SiC nanotube structures and also showcasing signifcant potential for further electronic properties calculations in this domain. Integrating ML algorithms with DFT-based approach presents an efcient algorithm for predicting total density of states in quantum-confned nanoscale structures. The fne tree regression algorithm shows highly efcient and efective prediction for density of states calculations