This article presents a theoretical framework for enhancing manufacturing productivity and safety by integrating cognitive ergonomics and artificial intelligence (AI). The framework addresses the critical gap in current research for a holistic approach to managing cognitive workload by proposing an integrated approach to managing cognitive workload and individual differences in modern manufacturing contexts. Despite the growing recognition of cognitive ergonomics, existing frameworks often fail to provide a unified model that incorporates AI and adaptive systems to optimize worker performance without negatively impacting mental workload or well-being. This paper proposes a comprehensive five-step theoretical framework integrating cognitive ergonomics principles, AI, and adaptive systems to optimize worker performance and productivity in manufacturing environments. The framework begins with assessing manufacturing operations and evaluates mental resource allocation to prevent overload. It optimizes cognitive and physical workload through real-time monitoring, integrates AI for dynamic task allocation, and establishes continuous feedback loops to adapt task demands, reduce errors, and enhance safety. This paper aims to contribute to developing predictive models for cognitive workload and tailored technologies for cognitive load reduction, filling a significant gap in existing research. The proposed framework provides a standardized approach to improving manufacturing operations by combining cognitive ergonomics with adaptive automation systems. Ultimately, it aims to promote worker well-being and performance, ensuring that AI and adaptive systems complement, rather than detract from, cognitive capacity while reducing cognitive workload, mental pressure, and performance declines in high-workload environments.