Abstract: The convergence of Artificial Intelligence (AI), drug discovery, and genomics represents one of the most transformative shifts in biomedical science. Traditional drug development is costly, slow, and fraught with uncertainty, yet AI-driven methodologies now promise to accelerate discovery, improve precision, and reduce attrition rates across the pharmaceutical pipeline. This paper provides an in-depth exploration of how AI enables predictive modeling of molecular interactions, target identification, and genomic-driven precision medicine. Machine learning algorithms, generative models, and systems biology frameworks are redefining the speed and scope of therapeutic innovation—allowing researchers to model diseases at the molecular level and design novel compounds with unprecedented accuracy. The study further examines the integration of genomics, transcriptomics, and proteomics data with AI, enabling stratified medicine and individualized therapy design. Ethical, regulatory. and data governance dimensions are critically assessed, including issues of data privacy, algorithmic bias, and explainability in medical AI systems. By analyzing current case studies and future trends, this paper reveals how AI not only accelerates drug discovery but also redefines the paradigm of innovation itself—ushering in an era of intelligent, personalized, and predictive medicine.