Artificial intelligence (AI) and pharmaceutical engineering, although we think of them as different fields, are increasingly difficult to integrate in order to improve healthcare. AI enables easier formulation of drugs and tries to advance precision medicine, playing a key role in all phases of drug development, from the identification of the structure itself to the prediction of toxicity. Traditional development is a very slow and complex process, while machine learning (ML) and deep learning (DL) models can simulate human intelligence and thus solve numerous challenges. The latest challenge is the unavailability of quality data for training artificial intelligence models, which is the current research goal. This paper explores how AI-driven formulation optimization, computational pharmacokinetics, and personalized treatment regimens can improve development efficiency and reduce manufacturing costs. Ensuring the safety, efficacy and quality of medical products is critical to market success, and artificial intelligence technologies help by analyzing large drug databases. ML and DL models are also crucial in predicting drug-drug interactions (DDIs) and preventing adverse drug reactions (ADEs), especially during pharmacokinetic studies. The integration of AI improves the accuracy of pharmaceutical regimens, thereby contributing to safer and more personalized drug use. By incorporating ML methods into pharmacology, AI provides deeper insights into the mechanism of drug action, which ultimately leads to clinical success. These techniques vary in complexity and computational requirements, making it important to choose the right algorithms for each application.