It is evident to figure out new ways that improve student performance and comprehend the elements that paves way to achieve success academically due to the growing significance of educational needs and catering to the expectations of researchers and educators. This study mainly focuses on exploring the approach of algorithm-based student academic predictions with machine learning. It is analyzed keeping various factors as the basis—including attendance, study habits, and parental involvement. The research uses a student performance dataset from Kaggle, consisting of 6607 records and 20 features. The dataset compilation is comprehensive in nature with the influential factors related to students’ exam performance as the key base. The study proposes a novel machine learning model to predict students’ final assessment scores. Data preparation is crucial, involving scaling, handling missing values, and adjusting variables. Feature selection identifies the most relevant attributes for improving model accuracy. Performance comparison with machine learning-based algorithms of different nature like random forests, nearest neighbor, support vector machines, Naïve Bayes, logistic regression, and k-nearest neighbor are included in this study. The resulting analysis highlights that linear regression is the most effective algorithm for predicting student performance, achieving a classification accuracy of 80–95%. The mean absolute error (MAE) is 0.53067, and the root mean squared error (RMSE) is 0.7284. The results show how machine learning may be used to predict academic performance and pinpoint important variables that affect academic success. This information empowers educators and institutions to create the essentials such as personalized learning experiences, interventions that are targeted in nature, and recommendations improvising on the educational outcomes. The study also highlights the importance of alignment of student’s performance in academics with employability skills and job market requirements.