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Social and Emotional Learning (SEL) is vital for students' academic success and overall well-being, yet assessing these competencies remains a challenge due to their subjective and non-cognitive nature. Traditional methods such as surveys and teacher observations often lack accuracy and consistency. To address this, a machine learning-based assessment framework is proposed, integrating diverse data sources such as student behavior, participation, and performance. By identifying patterns and classifying levels of SEL skills, machine learning models reduce human bias and enhance reliability. This data-driven approach not only ensures objective measurement but also provides predictive insights into students' SEL development. Educators can utilize these insights to design targeted interventions and personalized support, ultimately making the assessment of SEL competencies more accurate, scalable, and impactful.