With poverty being a key factor affecting access to basic education, more efforts were needed to enable students living in remote and rural areas to enroll in primary, middle, and high schools. A school quality improvement framework was intended to focus attention on measuring and addressing teaching and school facilities standards. Therefore, school level upgrading was also needed. In that study, the dataset used for analysis was constructed based on real data samples of Rakhine State Educational Data. As part of the analysis research, six classification algorithms of supervised machine learning (ML) were evaluated and chosen as the best model according to accuracy results. For improving performance scores of algorithms, four ensemble methods and Algorithms Tuning were performed.