The exponential growth of the World Wide Web has increased the need for efficient Web-page prediction models to reduce access latency and enhance user experience. Traditional methods, such as k-order Markov models, struggle with balancing prediction accuracy and complexity. In this work, we propose an ensemble model that combines Logistic Regression, Naive Bayes, and a First-Order Markov model to improve Web-page prediction accuracy. Logistic Regression identifies relationships in Web-log datasets, Naive Bayes applies probabilistic reasoning, and the Markov model captures transition probabilities between pages. By combining these models using a stacking classifier, we aim to leverage their strengths for more robust predictions. Our results demonstrate that the ensemble model outperforms individual models, achieving higher accuracy, precision, and recall. This hybrid approach can significantly improve Web navigation efficiency, making it a promising solution for real-time Web-page prediction.