This study examines teachers’ perceptions of Machine Learning (ML) tools within the context of Outcome-Based Education (OBE) to understand how these technologies are influencing instructional practices in contemporary classrooms. As educational systems evolve to meet the demands of the twenty-first century, ML offers transformative potential through personalized learning pathways, adaptive assessments, and real-time performance analytics. Using a qualitative approach, this study investigated how educators perceive the effectiveness, usability, and pedagogical alignment of ML tools. In-depth interviews, focus groups, and classroom observations were conducted to identify the perceived benefits and challenges faced by teachers in implementing the program. Key barriers include limited technical proficiency, difficulties with system integration, ethical concerns related to data privacy, and uncertainty surrounding the use of algorithmic decision-making. These findings highlight the critical role of professional development and institutional support in facilitating effective ML integration. Ultimately, this study contributes to the growing body of literature on AI in education by offering insights into how teacher readiness and perceptions influence the successful adoption of ML tools within the OBE framework.