The pharmaceutical industry faces significant drug discovery and development challenges, including high attrition rates, lengthy development times, and escalating costs averaging $2.8 billion per new drug approval. Artificial intelligence (AI) has emerged as a transformative technology to address these challenges by revolutionizing pharmaceutical modeling approaches. This review examines how AI technologies are implemented across the pharmaceutical modeling landscape, emphasizing PKPD (pharmacokinetic/pharmacodynamic) modeling, drug discovery, and formulation development. We analyze the technical underpinnings of AI applications, including machine learning, deep learning, and generative models, that are enabling more accurate predictions of drug properties, optimized formulations, and personalized dosing regimens. Recent industry implementations demonstrate significant efficiency gains, with McKinsey Global Institute estimating that AI could generate $60–110 billion annually in economic value for the pharmaceutical sector. Data quality, regulatory acceptance, and model interpretability remain despite these advances. This paper critically assesses AI’s current state and future trajectory in pharmaceutical modeling, highlighting both technical innovations and implementation considerations that will shape this rapidly evolving field.