The use of learning analytics and predictive analytics in the education sector especially higher education is not new, however, significant implementations have not happened in the past, and several educational institutions are still in the process or planning to bring learning analytics for achieving educational excellence. This research work has been carried out on a Dataset (OULAD) that was acquired from a university and uses various machine learning algorithm predictive capabilities to predict student results. A good educational environment requires accurate and early prediction of student performance. This predictive ability guarantees the best use of resources, enabling individualized learning experiences catered to specific needs. This research study intends to improve the model’s early prediction capabilities by introducing key features that are derived from the other features present in the dataset well before other Assessments and Final Exams. With a focus on the importance of early intervention, the enhanced model attained a noteworthy accuracy rate of 89.35%. This research study is a mere effort for the enhancement of predictive capabilities and enhancement of overall accuracy rate so that more educational institutions can benefit by implementing learning analytics and predictive analytics. This research study is intended to yield significant benefits for the student community by increasing the progression rate due to early intervention and measures.