Nanocomposites have developed as cutting-edge materials with unique features ideal for use in electronics, energy storage, catalysis, and biomedical engineering. This study demonstrates how the physicochemical and interfacial characteristics of nanocomposite systems can be investigated using a variety of computer approaches. Atomistic insights into electrical structures and interfacial interactions can be obtained using techniques like Density Functional Theory (DFT). At bigger scales, molecular dynamics (MD) simulations are useful for studying diffusive, mechanical, and thermal phenomena. Furthermore, long-time-scale phenomena like dispersion and aggregation behavior can be studied using Monte Carlo simulations and coarse-grained modeling. Rapid screening and formulation optimization for nanocomposite materials are made possible by the growing adoption of machine learning methods for predictive modeling. The adaptability of nanocomposites across a range of domains has been proved by recent computational research. For example, DFT and molecular docking studies of B-CuO/rGO nanocomposites demonstrated their effective antibacterial potential and appropriate electrical characteristics for photocatalysis. Semi-interpenetrating nanocomposite hydrogels for bone tissue engineering have been optimized using machine learning, improving regeneration results through data-driven design. Graphene oxide nanosheets also showed good stability and bioactivity with a HOMO-LUMO gap of 2.806 eV. GQD–Pt(II) nanocomposites investigated through DFT show an improved photovoltaic performance for renewable energy production, with the electron-donating groups reaching energy conversion efficiencies up to 24.6%. These illustrations show how computational methods can direct material design, forecast structure–property connections, and foster innovation. Computational studies on nanocomposites constitute an engaging and developing field of study that has enormous potential for future technological breakthroughs and justifies more research because of its capacity to provide in-depth insights at low experimental costs.