The proliferation of digital technologies has led to an increasingly sophisticated cyber threat landscape, rendering traditional signature-based detection methods inadequate. Machine learning (ML), particularly ensemble learning, has emerged as a promising paradigm for developing robust and adaptive cyber threat detection systems. This paper provides a comprehensive review of ensemble learning techniques for cyber threat detection, offering a systematic analysis of the current state-of-the-art and identifying future research directions. We conduct a thorough review of the literature, categorizing and analyzing various ensemble methods, including bagging, boosting, and stacking, and their applications in cybersecurity. Our analysis reveals that while significant progress has been made, many existing models are limited to single-task learning or shallow hybridization, resulting in moderate prediction accuracy and high false-positive rates. This review highlights the need for more advanced, multi-layered ensemble models that can effectively address the complexity and dynamism of modern cyber threats. We conclude by outlining key challenges and opportunities for future research, including the development of scalable and interpretable ensemble models, the integration of deep learning techniques, and the creation of standardized evaluation benchmarks.