Student performance prediction has played a massive influence on students risk management and academic outcomes. Traditional prediction models have limitations in infrastructure and data processing abilities which leads to disorganization in handling large educational datasets. The datasets consist of non-relevant data which can impact the prediction capabilities of the organization. The prediction has to be done with maximum accuracy. Therefore, a constructive technique must be used to improve the datasets for enhanced clarity. This study introduces a cloud computing solution to address these challenges. By utilizing the adaptability of cloud platforms, ample amount of student data, including grades, participation, and attendance, can be prepared in the database. The cloud system uses machine learning algorithms to provide more accurate predictions. The proposed solution also confirms the security and privacy of student data through innovative encryption and control mechanisms, managing the growing concerns surrounding data handling in education. This method provides a reliable framework for predicting student performance, improving the ability to involve and support students effectively.