Traditional drug discovery processes have disadvantages such as efficiency, cost, and high attrition rates. In silico methods, involving computational simulations and modelling, offer powerful solutions to bridge the gap between discovery and development. This review explores various in silico approaches, including ligand-based and structure-based drug design, virtual screening, molecular docking, and ADMET prediction. We explore their utilization throughout different phases of phar-maceutical development, spanning from target identification and lead refinement to forecasting tox-icity and pharmacokinetics. In-silico methods enable rapid lead identification and optimization, re-ducing reliance on expensive wet lab experiments. They contribute to improved drug quality by pre-dicting ADMET properties and off-target effects, ultimately accelerating development timelines and lowering costs. In silico approaches are revolutionizing drug design by providing predictive and cost-effective solutions. Incorporating them into the design process streamlines lead refinement and en-hances the likelihood of success for potential drugs, ultimately expediting the translation of innova-tive treatments to patients.