This chapter examines the transformative impact of machine learning (ML) on educational outcomes in primary and secondary schools, particularly within the framework of outcome-based education (OBE). Through the use of ML, teachers can create evidence-based methods for individualizing learning experiences so that teaching strategies match student needs. Predictive analytics based on ML allow for the early detection of students who need support, making interventions possible earlier to improve academic performance. Intelligent automation also automates administrative tasks, allowing teachers to devote more time to pedagogy. The chapter delves into the contribution of ML towards enhancing student engagement through adaptive learning platforms, sentiment analysis, and behavior prediction, resulting in better classroom management. In addition, it spotlights how ML algorithms can process big datasets to determine teaching effectiveness, optimize curriculum development, and enhance learning approaches. Ethical issues, data privacy, and fair access to AI-driven educational tools are also addressed to ensure the responsible deployment of these tools. Educators and policymakers can incorporate ML into OBE to improve student outcomes, develop critical thinking capabilities, and build a more inclusive and effective learning system. Ultimately, this chapter provides insight into how ML-based solutions can redefine conventional teaching approaches, making education more adaptive, student-focused, and outcome-driven.