The traditional drug discovery process is inherently characterised by complexity, high costs, lengthy timelines, and a low success rate. Artificial intelligence (AI) and machine learning (ML), a subset of AI, offer transformative potential to address these persistent challenges. By leveraging techniques such as deep learning (DL) and Natural Language Processing (NLP), AI systems can analyse vast datasets, accelerate timelines, reduce costs, and significantly increase the efficiency and success rates of pharmaceutical research. AI applications span the entire drug discovery pipeline, from identifying molecular targets and screening compounds to predicting toxicity, optimising formulations, and enhancing clinical trials. While AI holds the promise of delivering safer, more effective, and more accessible medicines, its integration faces critical hurdles related to data quality, algorithmic bias, model interpretability ("black box" issues), and the development of adequate regulatory frameworks.