Data-driven production planning, by leveraging modern technologies such as artificial intelligence, machine learning, and big data analytics, enables the optimization of production processes, improved demand forecasting, and effective resource management. In the biopharmaceutical industry, which faces considerable uncertainties such as market fluctuations, supply chain complexity, and qualitative changes in production, the adoption of data-driven approaches can create a significant competitive advantage. Recent studies have shown that many companies encounter major challenges in adopting data-driven technologies and planning for data-driven production. Much of these difficulties are related to the characteristics of the production planning environment; therefore, understanding these characteristics is essential for identifying the needs and opportunities of data-driven production planning. Accordingly, the primary objective of this article is to propose an optimization model for data-driven production planning based on the characteristics of the production planning environment in the biopharmaceutical industry. In the present research, a case study (descriptive–mathematical) was conducted in the pharmaceutical industry. Data were collected through observations and visits to production sites, workshops, meetings, and formal interviews with production managers and planners, supply and technology managers, innovation managers, and production line engineers. The problem data were analyzed using CPLEX GAMS 24.4 software and a genetic algorithm implemented in Matlab R2022b. Ultimately, a conceptual, data-driven, and intelligent model (instead of the traditional model) of production planning based on production flexibility, intelligent systems, and expert systems is presented.