The application of big data analytics within the field of education has gained significant traction in recent years. This paper examines the integration of big data analytics into teacher evaluation processes with the aim of enhancing both instructional effectiveness and student achievement. It evaluates the potential advantages and limitations associated with employing big data analytics for assessing teacher performance, and proposes a comprehensive model tailored to this application. The suggested model encompasses stages such as data collection, cleaning, integration, analysis, quality assessment, and final teacher evaluation. A key focus of the model is the critical role of data quality in ensuring accurate and meaningful evaluations, while also addressing issues related to privacy concerns, inherent biases, technical competencies, and stakeholder acceptance. Additionally, the paper explores how big data analytics can facilitate data-driven decision-making in education, enabling leaders to make evidence-based choices regarding resource distribution, professional development programs, targeted interventions, and curriculum enhancement.