Outcome-based education (OBE) focuses on defining specific learning outcomes for students and continuously modifying teaching and assessment methods to achieve these outcomes. As one of the most transformative influences on education, the digitalization of the learning process is growing exponentially, and with it, the possibilities for new meaning through the application of Machine Learning (ML) for data-driven decision-making, adaptive and predictive learning can help to improve the efficiency of OBE. This chapter discusses the integration of ML and OBE as a collaborative agent for advanced algorithms that automate assessments, identify at-risk learners, provide real-time feedback, and personalize learning pathways. It also raises fundamental questions about data privacy, algorithmic fairness, and institutional willingness to adopt a new technology. The systematic roadmap for implementing ML-powered OBE frameworks includes strategic contingent plans, ranging from readiness assessment to full-scale deployment. The chapter concludes with a vision of a learning future characterized by intelligent, self-evaluating, student-centric, and lifelong education systems that yield measurable gains in the educational process.