In this chapter, we discuss how machine learning (ML) can serve as a robust and efficient aid to bring out and enhance outcome-based education (OBE), thereby improving the quality of education in the current era. There is a 15% increase in demand for personalized learning, resulting in a growing need for an adaptive approach. In a traditional education system, addressing learners’ needs is typically challenging. For example, research suggests that 40% of students feel they have very little exposure to personalized learning. ML, an edtech company, has contributed to customized learning and adaptive learning through real-time monitoring and fluid learning pathways. ML has been an approach used by educators and policymakers to accommodate the changing needs of individual learners (ML). While the literature is still developing, case studies suggest student performance improved when participating in OBE peer learning with an emphasis on personalized learning utilizing ML. Improvement was reported for some students and groups of learners by as much as a 25% increase in overall performance. Hence, it demonstrates how an adaptive learning experience can yield effective learning outcomes. Of course, there are ethical concerns, especially in terms of data privacy, and equitable access to educational support—estimates indicate that as many as 30% of educational settings face these challenges. Therefore, transformative and equitable education must occur in conjunction with ML—utilizing accessible and responsive systems—but must also be systematic and responsible in the use of data and infrastructure. It will need to continue to be the research that ML is efficacious and equitable in education and beyond.