Cancer is a multifaceted disease driven by complex genetic and molecular alterations, disrupting key signalling pathways such as PI3K/AKT/mTOR and RAS/MAPK. These pathways present critical targets for therapeutic intervention. The development of anti-cancer drugs has been revolutionized by in silico approaches, which provide a transformative and strategic advantage in identifying and optimizing potential therapeutic compounds, including molecular docking, molecular dynamics, QSAR, pharmacophore modelling, and deep learning, that have transformed cancer drug discovery by accelerating the identification and optimization of potential therapeutics. Furthermore, techniques like CoMFA and CoMSIA provide 3D QSAR insights, highlighting critical molecular features for activity enhancement. Drug repurposing leverages existing drugs for novel cancer therapies, offering cost-effective and expedited solutions. Recent studies demonstrate the efficacy of combining these computational approaches, achieving robust predictive models and identifying potent anti-cancer agents. Advances in artificial intelligence and machine learning have enabled precise predictions of drug–target interactions, biomarker identification, and drug sensitivity, facilitating personalized medicine. Recent breakthroughs, such as AlphaFold-driven protein structure prediction and deep learning models like DTiGEMS+ and MultiSurv, have demonstrated unparalleled accuracy and scalability in drug discovery. The convergence of computational methodologies and molecular insights represents a paradigm shift in anticancer drug discovery. These approaches collectively accelerate the identification and optimization of drug candidates, offering cost-effective, scalable, and precise solutions. As computational tools evolve, their integration into drug discovery workflows promises a future of more efficient, targeted, and personalized cancer therapeutics.