Drug discovery is time-consuming and resource-intensive, but computational approaches offer a more efficient alternative. The urgency for antiviral treatments became evident during the SARS-CoV-2 pandemic due to the virus’s rapid spread and mutations. This study utilizes computational drug design techniques to assess the antiviral potential of Tinospora sinensis constituents against the SARS-CoV-2 main protease (PDB ID: 6LU7). The target protein was prepared using AutoDock tools, and molecular docking was conducted with AutoDock Vina. Of 37 compounds, 5 exhibited a binding affinity below -7 kcal mol-1, with tinosporaside showing the highest affinity and low toxicity. These results suggest that tinosporaside is a promising candidate for further development. By streamlining drug discovery, computational methods accelerate the identification of potential treatments, reducing costs and waste. This study underscores the value of computational methods in antiviral research and supports further investigation into combating SARS-CoV-2 and future viral threats.