Higher education institutions face accelerating skills change, expanding digital delivery, and rising expectations for workforce relevance. This study examines how machine intelligence can support predictive–adaptive, data-driven curriculum design by integrating learning analytics, adaptive learning platforms, and institutional decision systems. Evidence from the analysis indicates that predictive models strengthen alignment between program outcomes and labor-market needs, while adaptive learning features improve student engagement and learning support. At the institutional level, analytics-enabled planning enhances resource allocation, training design, and operational efficiency. Qualitative insights further highlight perceived benefits (personalization, timely feedback, and transparency) alongside persistent barriers, including data quality constraints, implementation capacity, governance and ethics, and uneven access that may reinforce inequities if not addressed. The findings imply that effective adoption requires clear data governance, stakeholder readiness, faculty development, and equity-by-design safeguards. Overall, machine intelligence is positioned as an enabling infrastructure for continuous curriculum improvement when paired with responsible oversight and inclusive implementation.