Artificial Intelligence (AI) has rapidly evolved into a transformative force in pharmaceutical research, reshaping the landscape of drug discovery and development. Leveraging advances in machine learning (ML) and deep learning (DL), AI enables the efficient analysis of massive biological and chemical datasets, facilitates accurate target identification, and accelerates lead compound optimization. Traditional drug discovery is costly, time-consuming, and characterized by high attrition rates, whereas AI-driven approaches significantly enhance prediction accuracy and decision-making efficiency across all stages of drug R&D. Recent breakthroughs such as AlphaFold3, RFdiffusion, ProteinGenerator, and PISTE exemplify how AI can precisely predict protein structures, model biomolecular interactions, and design novel therapeutic candidates with unprecedented speed and reliability. Despite these achievements, challenges remain in data accessibility, model interpretability, and multi-objective optimization. Addressing these issues will be key to realizing AI’s full potential in ushering in a new era of intelligent, efficient, and low-cost drug development.