The Performance of Student Analysis is a powerful web-based method designed to provide educators, administrators, and stakeholders with a precious tool for assessing and understanding student accomplishments and development. The paper utilizes machine learning algorithms, data processing technologies and easy-to-use interfaces to upload, process, and examine student performance data. Users upload data files that contain the metrics of student’s performance, or they manually enter the student data in the system to be analyzed. The system uses machine learning models like K-Nearest Neighbors algorithm to predict the grade a student gets with the optimum nearest neighbors. The system tracks and analyzes performance indicators – grades in each subject, attendance records, and even behavior. This process helps find outliers that might indicate students who need extra attention and help. Furthermore, the system can chart grade distributions so that the results can be easily interpreted.