Pharmaceutical formulation development involves several stages, including preformulation, excipient screening, dosage form design, development, and evaluation. The goal is to achieve a quality drug product that is stable, safe, effective, and in a pharmaceutically acceptable dosage form. By using appropriate mathematical and computational models, we can save valuable time and resources compared to conventional trial and error method and Quality by Design (QbD) based formulation development approaches. In a new research direction where, “Quantitative structure-property relationship” (QSPR), “Quantum mechanics” (QM), “Molecular mechanics”, “Machine learning” (ML), and “Artificial Intelligence” (AI) have been utilized in the field of formulation development. These tools provide a better prescience of the scientific mechanisms underlying the in-vivo conduct of drug products, which is the cornerstone of formulation development. Thanks to modern computational modeling, development timelines and costs have been shortened, and research efficiency has improved. This has been witnessed in the development of vaccines for recent pandemics such as Covid-19. Even though, it’s a long way before regulatory acceptable computational tools in formulation development, Various computational models are developed, trained, and validated with good prediction accuracy. The experimental values exhibit a high level of conformity with the predicted results. However, continuous improvement in existing models is essential before applying them in practice. This chapter aims to outline the different computational modeling approaches applied in designing formulations for oral, inhalational, ophthalmic, dermatological, parenteral, nano-based formulations, and biological molecules.