With the rapid digitization of enterprises and services, the risk of information leakage has escalated, threatening organizational security and individual privacy. This paper gives an in-depth research on modern tools and techniques applied to predict information leakage with specific focuses on rule-based systems, statistical models, machine learning techniques, cryptographic mechanisms, and hybrid structures. All of these categories are explicitly compared and contrasted with key parameters such as predictive accuracy, scalability, false-positives and adaptability. What is more, the exploration takes into account the new trends of federated learning, blockchain technology, and zero-trust security designs, which promise considerable opportunities in the improvement of leakage detection prospects in the future. Empirical validation is obtained by considering the actual case studies in practice, thus, explaining both the effective applications and the failures that were observed, and providing practical information about the challenges in operations and situations of deployment. On the basis of the evidence obtained, the paper suggests a strategic map whose goal is to increase predictive abilities without violating international data protection laws at the same time. The results eventually support a multi-layered, adaptive, and context-aware defensive pose to the information leakage and the importance of solutions that are technically solid and at the same time consistent both with organizational goals and ethics.