Millions of dollars and valuable resources are being wasted annually due to ineffective requirement prioritization methods in managing the complexities of Complex Adaptive Systems (CAS). Traditional prioritization approaches struggle significantly to adapt to the dynamic and evolving nature of Critical Asset Systems (CAS), especially within critical industries such as aerospace, defense, healthcare, and environmental management. These rigid methods often lead to project delays, budget overruns, and suboptimal performance, ultimately failing to meet stakeholder expectations. This research introduces a transformative approach by integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques into the prioritization process, aiming to dramatically enhance adaptability and responsiveness. Utilizing a mixed-methods strategy, this paper provides empirical validation through detailed mathematical modeling, quantitative assessments, and in-depth qualitative case studies. Notably, AI and ML-driven prioritization frameworks have shown marked improvements in real-time adaptability, operational efficiency, and resource allocation compared to traditional methods. Additionally, the
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