Abstract
This study systematically explores the computational investigation of carbon nanomaterials (CNMs), including carbon nanotubes, graphene, fullerenes, carbon nanofibers, quantum dots, and nanodiamonds for innovative utilizations. Dwelling on computational approaches such as molecular dynamics (MD), density functional theory (DFT), and finite element modeling (FEM), the review highlights the ability of these simulation methods to enhance innovative applications of CNMs in the medical sector for drug delivery, electronic, and biosensors; energy sector for supercapacitors, batteries, and fuel cells; and green environment for remediation adsorbents and catalysts. The combination of multiple simulation approaches and machine learning (ML) is crucial in optimizing the system’s ability to forecast innovations and speed up smart development of CNMs for improved sustainability. By outlining the correlation between atomic‐level modeling and practical world usage of CNMs, this research fills the gap between theoretical and practical nanotechnology. It reiterates the role of computational modeling in bringing down costs of laboratory experiments, enhancing material functionality, and providing robust decisions during the design of CNMs for specific applications. Model limitations, data combination, data scarcity, and lack of experimental validation were some of the challenges of computational modeling of CNMs discussed in the study. This comprehensive review presents a robust platform for scholars who intend to harvest the wholesome prospects of CNMs via modeling and simulation techniques.