The pharmaceutical drug development process is highly complex, costly, and time-intensive, often taking over a decade and costing billions of dollars to deliver a single drug to market. Artificial intelligence (AI) and machine learning (ML) have emerged as revolutionary forces capable of accelerating this pipeline by enabling data-driven progression in the field of drug discovery, formulation design, and precision medicine. This article overviews the recent advances in AI-based techniques and their integration across key stages of pharmaceutical research and development. In the drug discovery field, AI has improved target identification, virtual screening, and drug design through deep learning architectures. These tools facilitate accurate predictions of drug–target interactions, toxicity, and pharmacokinetic behaviour, thereby reducing attrition rates and experimental burden. AI-driven drug repurposing leverages existing safety and pharmacokinetic profiles to identify new therapeutic uses for approved drugs, offering a faster and cost-effective alternative to traditional drug discovery. AI analyzes large-scale genomic and clinical data to enable the design of personalized therapies tailored to an individual’s genetic makeup. AI also plays a critical role in pharmaceutical formulation and drug delivery by optimizing excipient selection, processing parameters, and controlled-release profiles. These applications extend to advanced delivery systems, including nanomedicines, where AI techniques improve bioavailability and targeting efficiency. AI is also reshaping clinical trial design through optimized patient recruitment, adaptive monitoring, and personalized dosing strategies. Despite these advancements, challenges related to model interpretability, algorithmic bias, and ethical governance remain critical barriers to widespread adoption. Overall, this article highlights AI as a central catalyst for innovation in pharmaceutical development, with the potential to enable precision medicine and advance global healthcare through responsible implementation.