Artificial Intelligence (AI) has become a game-changer in drug discovery and development by speeding up the design, testing, and optimization of new medicines. Using machine learning, deep learning, and generative models, AI can analyze large and complex biological, chemical, and clinical datasets with high accuracy. This not only lowers research costs but also reduces the time needed to develop new drugs. Major applications include target identification, virtual screening of compounds, lead optimization, ADMET prediction, drug repurposing, and personalized medicine. Popular AI-based platforms such as AlphaFold, Atomwise, BenevolentAI, and Insilico Medicine have shown real-world benefits, like rapid protein structure prediction, virtual screening, and identifying potential drug candidates. AI is also contributing to precision medicine by predicting patient-specific responses and minimizing side effects.Despite its benefits, challenges such as poor data quality, limited interpretability of models, high computational needs, ethical concerns, and lack of generalizability still exist. Overcoming these issues is crucial for wider adoption.Looking forward, combining AI with quantum computing, multi-omics analysis, and advanced clinical trial simulations can make drug discovery even faster and more precise. Overall, AI is shifting from just a supportive tool to a core driver of pharmaceutical innovation, offering new opportunities for developing safer and more effective therapies.