The use of predictive analytics in e-learning environments presents an innovative approach that can transform the educational process by enabling learners to access personalized learning opportunities anytime. This paper proposes a future-oriented framework that integrates machine learning algorithms with adaptive content delivery systems to anticipate learners’ future educational needs and tailor learning plans accordingly. Statistical analysis highlights the predictive power of the models used. XGBoost emerged as the most reliable model, achieving perfect metrics with an accuracy, precision, recall, and F1 score of 1.0. The Random Forest model also demonstrated excellent performance, with an accuracy of 0.992, precision of 1.0, recall of 0.846, and an F1 score of 0.917. The Neural Network (MLP) yielded impressive results, achieving an accuracy of 0.972, precision of 0.75, recall of 0.692, and an F1 score of 0.72. However, there is room for improvement in recall and F1 scores, particularly for models like Support Vector Machine and Logistic Regression, which had respective accuracies of 0.968 and 0.965. Leveraging data on learner behavior, performance, and engagement, the proposed framework dynamically adapts content delivery, learning pace, and assessments to align with individual learners’ skills and tendencies. The implications for education systems are significant, including enhanced scalability, accessibility, and inclusion. Additionally, this study explores the potential of lifelong learning and workforce training in the context of digital transformation.