Die filling is a critical stage in tablet compression, where the uniformity of powder distribution within the die cavity directly affects the tablet weight, hardness, content uniformity and complete quality. Traditional methods of optimizing this process can be time-consuming and prone to human error. Computational modeling of pharmaceutical die filling processes plays a crucial role in enhancing the efficiency, precision and scalability of solid dosage form manufacturing, particularly in tablet production. In this chapter computational modeling techniques were discussed, including discrete element modeling (DEM), computational fluid dynamics (CFD), finite element analysis (FEA) and CFD-DEM models provide advanced tools to simulate and predict the behavior of powders during die filling, to help in understanding the complex interactions between particles, the effects of powder flowability and the influence of machine parameters such as die geometry, fill height and compression forces. By leveraging these computational approaches, manufacturers can optimize die filling conditions, minimize variability in tablet weight, improve process control and reduce material waste. Artificial intelligence (AI)/Machine learning (ML) integration in pharmaceutical die filling optimization, by analyzing vast datasets from real-time sensors and historical production data for predictive modeling of new formulations enables faster development with minimal errors through AI models provides adaptive process control, adjusting parameters to improve consistency and product quality. Implementation of real-time process monitoring combined with closed-loop control systems to continuously adjust parameters during die filling. The computational modeling in die filling processes, emphasizes the design of more efficient, robust, and scalable tablet production systems, contributing to the development of high-quality pharmaceutical products.