The ever increasing importance of education has driven researchers and educators to seek innovative methods for enhancing student performance and understanding the factors that contribute to academic success. This paper presents a methodology for predicting CGPA SGPA that leverages machine learning techniques to forecast students' academic achievements based on a variety of features, such as demographic information, academic history, and behavioural patterns. The proposed students academic performance method utilizes a real-world collected dataset from multiple educational institutions to ensure an accurate and comprehensive analysis. The proposed methodology starts with a data preparation stage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables if necessary. The feature analysis technique was used to select the most important features for the students academic performance model. A number of machine learning classifiers were tested, and the feature analysis was found to be the best performer. The results of this study demonstrate the potential of algorithms in predicting student performance and identifying key factors that influence academic success. This information can be leveraged by educators and academic institutions to develop targeted intervention strategies, tailored learning experiences, and personalized recommendations for students, ultimately fostering a more effective learning environment and improving overall educational outcomes.