Artificial Intelligence (AI) is transforming the pharmaceutical industry by accelerating drug discovery, optimizing drug delivery, and enabling personalized medicine. This review discusses the applications of AI in drug discovery, target identification, personalized medicine, drug safety, and formulation. AI techniques such as machine learning, deep learning, and predictive modeling are being used to analyze vast datasets, identify potential drug targets, predict drug efficacy, and optimize drug formulations. AI has significantly improved the efficiency and accuracy of drug discovery processes, reducing the time and cost associated with traditional methods. It has also enabled the development of more effective and targeted drug delivery systems, including nanocarriers and microfluidic devices. AI is also being used to personalize treatments based on individual patient characteristics, such as genetic makeup, lifestyle, and other factors. However, challenges such as data quality, model interpretability, and regulatory considerations remain. Despite these challenges, AI is expected to play an increasingly important role in the future of pharmaceutical research and development, improving patient outcomes and reducing healthcare costs.Artificial Intelligence (AI) is transforming the pharmaceutical industry by accelerating drug discovery, optimizing drug delivery, and enabling personalized medicine. This review discusses the applications of AI in drug discovery, target identification, personalized medicine, drug safety, and formulation. AI techniques such as machine learning, deep learning, and predictive modeling are being used to analyze vast datasets, identify potential drug targets, predict drug efficacy, and optimize drug formulations. AI has significantly improved the efficiency and accuracy of drug discovery processes, reducing the time and cost associated with traditional methods. It has also enabled the development of more effective and targeted drug delivery systems, including nanocarriers and microfluidic devices. AI is also being used to personalize treatments based on individual patient characteristics, such as genetic makeup, lifestyle, and other factors. However, challenges such as data quality, model interpretability, and regulatory considerations remain. Despite these challenges, AI is expected to play an increasingly important role in the future of pharmaceutical research and development, improving patient outcomes and reducing healthcare costs.