This chapter explores the integration of Machine Learning (ML) into Outcome-Based Education (OBE) to address its implementation challenges and enhance its effectiveness. OBE focuses on clearly defined learning outcomes, promoting student-centric, skill-based learning aligned with industry standards. However, its adoption often compromises the quality of learning experiences due to limited assessments and fragmented feedback systems. To overcome this, the chapter proposes ML techniques that offer personalized feedback, formative assessments, and predictive modeling for student success. Despite their potential, ML models pose challenges like data bias, low interpretability, and privacy concerns. The chapter critically examines how such issues affect learning outcomes and proposes bias mitigation and fairness-aware algorithms to ensure equitable education. Through real-world examples and use cases, it demonstrates how ML enhances adaptive curriculum design, learning analytics, and student performance predictions. The results show improved accessibility, relevance, and inclusivity in education. However, the “black-box” nature of ML remains a barrier for educators to trust and utilize these tools fully. The chapter concludes by highlighting future directions for integrating interpretable and ethical ML solutions into OBE frameworks, encouraging more transparent, inclusive, and outcome-driven learning systems. This interdisciplinary approach redefines education, offering transformative value for students, educators, and institutions alike.