<p>Time management is, therefore, one of those important skills that help a student improve their academic performance, which in turn leads to better results. The present article discusses effective time management strategies that lead towards improved academic outcomes using predictions based on machine learning algorithms. Students able to master time management achieve optimized study habits, suitable time allocation for different types of tasks and proper work-life balance. Time management allows learners to be in control of their time, to distribute it among their academic responsibilities, to set realistic goals and to increase their productivity. Moreover, it teaches students how to meet deadlines, to overcome procrastination, and to enhance their attention and concentration throughout study sessions. This study develops estimates of the impact that time management has on academic performance using Naïve Bayes classification (NBC), logistic regression classifier (LRC), extra tree classification (ETC), random forest classifier (RFC), support vector classifier (SVC) and <i>K</i>-nearest neighbor classification (KNNC) techniques in concert with trochoid search optimization (TSO) and Bayesian optimization (BO). These results reflect that the random forest with Bayesian optimization model with an astonishingly great mark of 0.931 in the training phase stands out as the best to evaluate the impact of time management. Then comes, not too far behind, the extra trees with Bayesian optimization model at an accuracy rate of 0.908. On the contrary, the lowest performance was established by LRTS (LRC + TSO) with an accuracy score of 0.529. These findings pinpoint effective time management as an essential attribute to improving academic performance and achievement among students.</p>