Scientific research and engineering innovations increasingly depend on complex workflows that require coordination across many types of computing resources, ranging from small edge devices to powerful supercomputers. However, the growing complexity of these workflows and the distributed nature of the underlying computing and data cyberinfrastructure (CI) make it difficult for many researchers and engineers to fully access, manage, and benefit from these advanced technologies. The goal of iWMS is to apply new advancements in Artificial Intelligence (AI) techniques to design and implement a workflow management framework that empowers researchers, engineers, and educators to take advantage of the computing continuum for scientific discovery, innovation, and education. The framework promotes progress in science and engineering, powering applications that advance national priorities. The project designs and implements iWMS, an open, modular workflow management framework that integrates AI across the entire workflow lifecycle. In particular, iWMS incorporates AI models to support automated workflow composition, intelligent resource provisioning, performance prediction, real-time anomaly detection, and workflow adaptation. Techniques such as retrieval-augmented generation facilitate workflow discovery and assembly, while machine learning-based planning and monitoring services optimize execution and enhance system reliability. With the framework foundation built on a modular and i