Penicillium chrysogenum is the principal microorganism used for the industrial production of penicillin. The widespread use of this fungus has led to certain challenges regarding antibiotic production. As the global demand for antibiotics continues to increase, the importance of nutritionally balanced as well as cost effective culture media becomes increasingly evident. Traditionally, different optimization methods have been used for culture media, many of which have been useful and have improved our current knowledge of the matter. These methods have mostly been based on experimentation and refinement. However, large-scale manufacturing requires higher efficiency in terms of time and resources, as well as higher precision. In recent years, the emergence of modern computational tools such as genome-scale metabolic models (GSMMs) has greatly improved our ability to understand and predict fungal and microbial metabolic behavior, which in turn has led to improvements in the optimization of manufacturing processes. In this study, a GSMM of P. chrysogenum was used to identify optimal nutrient settings that theoretically enhance penicillin production under defined uptake constraints. The model combined metabolic pathway analysis with flux balance analysis to evaluate different culture conditions. Among the tested conditions, maltotriose and urea were identified as the most effective carbon and nitrogen sources and showed better cellular growth kinetics and higher antibiotic production rates. Further laboratory-scale validation confirmed the predicted qualitative trends, with penicillin titers increasing by up to 11% under optimized sucrose–urea conditions compared to the baseline medium. The findings highlight the utility of GSMM-driven approaches for medium design and optimization. This predictive framework can help reduce costs and accelerate the development process in biomanufacturing and biotechnology.