The importance of the teaching–learning process in shaping outcomes is critical, necessitating the development of new evaluation methods for effective implementation. This paper presents a framework for evaluating and optimizing a smart teaching–learning ecosystem, utilizing data analytics and AI methodologies facilitated by smartwatches. Wearable technology is used to capture real-time physiological and behavioral metrics (e.g., heart rate, physical activity, and attention levels) from students during classroom instruction. Artificial intelligence algorithms analyze this data to assess engagement, cognitive load, and overall responsiveness to various instructional methods. These insights are synthesized into actionable feedback for educators, providing information that can enhance pedagogical strategies in a manner that aligns more closely with learner needs. This facilitates the examination of trends and anomalies among various learner types to enhance inclusivity in education. This study illustrates the practicality of employing data analytics alongside wearable technology to develop a comprehensive methodology for evaluating the effectiveness of learning and teaching. The preliminary results demonstrate the system’s ability to provide accurate, scalable, and real-time insights, advancing beyond statistical analyses to support evidence-based educational interventions. This solution represents a significant advancement in the modernization of academic assessment, integrating technology and pedagogy.