Artificial Intelligence (AI) has emerged as a disruptive technology in pharmaceutical scienc-es, especially in formulation development. Traditional formulation strategies are largely em-pirical and trial-and-error in nature which are time-consuming, costly and often inefficient. AI includes machine learning (ML), deep learning (DL) and artificial neural networks (ANN) integration allows predictive modelling, optimization of formulation parameters and reduce experimental workload(Gunjal, 2025).AI based systems can predict Drug Excipients compatibility , Dissolution profiles , Stability and Critical Quality attributes efficiently accelerating the development process. Additionally, AI aids in the design of advanced drug delivery systems such as nanoparticles and controlled-release formulations by optimizing design parameters and enhancing therapeutic outcomes.Even with these advantages, challenges of lack of data, model explainability and regulatory approval remain significant barriers. But the ongoing advances and increasing adoption of AI in pharmaceutical industries show its strong potential to revolutionize formulation develop-ment.(Sartaj et al., 2025).This review provides a comprehensive overview of the applications of AI in pharmaceutical formulation, discusses the key techniques, highlights the applications in industry, and dis-cusses the future perspectives. The article also highlights the existing research gaps and regu-latory considerations to guide the future development in this fast-changing field.(Sartaj et al., 2025).