Despite global disruptions caused by the COVID-19 pandemic, education sector adapted and continued to function online. Undergraduate courses happened online. Variations in students’ performance emerged due to differing approaches in incorporating online education into routine life amidst challenging conditions at home and in society during the pandemic. It was necessary to find out whether online education aided academics. A solution based on a machine learning is proposed and implemented in the paper to forecast an individual’s performance using data gathered from a user survey among university students regarding their perceptions of online education. The user survey comprised questions about factors that could affect academic performance. A diverse range of algorithms are meticulously evaluated to identify one that delivers precise predictions of student performance, leading to an effective restructure of educational landscapes to replicate the immersive and engaging experience of in-person classes, fostering a conducive environment for students to wholeheartedly focus on academic pursuits.