Big Data and Machine Learning in Defence In the era of data-driven warfare, the integration of big data and machine learning (ML) is crucial for enhancing defence capabilities. This research explores the applications of big data and ML in the defence sector, focusing on their potential to revolutionize intelligence gathering, strategic decision-making, and operational efficiency. These technologies offer opportunities for threat detection, predictive analysis, and optimized resource allocation. However, their adoption raises concerns about data privacy, ethical implications, and potential misuse. The integration of big data and ML in defence operations is significant for national security, enhancing situational awareness, identifying threats, and supporting decision-making. The research aims to provide a comprehensive understanding of the current state of big data and ML in defence, while examining the challenges and ethical considerations that must be addressed for responsible implementation. Big data and ML are currently being utilized in defence applications such as intelligence gathering, predictive analytics, cybersecurity, and logistics. They offer benefits like enhanced situational awareness, threat detection, and optimized resource allocation. However, challenges include data quality, integration, computational requirements, privacy, and ethical issues like algorithmic bias. The literature review highlights the growing interest in leveraging big data and ML for defence applications. It identifies key areas of application and discusses the benefits and challenges associated with their integration. The review also emphasizes the need for ethical guidelines, regulatory frameworks, and responsible innovation to ensure the responsible use of these technologies. Recommendations for policymakers, defence organizations, and researchers include developing regulatory frameworks, implementing robust data governance, and fostering interdisciplinary collaboration. Ethical guidelines emphasize data privacy, transparency, accountability, and the responsible use of ML systems. Future research should focus on advanced data integration techniques, emerging ML methods, and the ethical implications of AI in defence. The responsible and ethical adoption of big data and ML in defence is essential to ensure national security while up to ethical and legal standards.Big Data and Machine Learning in Defence In the era of data-driven warfare, the integration of big data and machine learning (ML) is crucial for enhancing defence capabilities. This research explores the applications of big data and ML in the defence sector, focusing on their potential to revolutionize intelligence gathering, strategic decision-making, and operational efficiency. These technologies offer opportunities for threat detection, predictive analysis, and optimized resource allocation. However, their adoption raises concerns about data privacy, ethical implications, and potential misuse. The integration of big data and ML in defence operations is significant for national security, enhancing situational awareness, identifying threats, and supporting decision-making. The research aims to provide a comprehensive understanding of the current state of big data and ML in defence, while examining the challenges and ethical considerations that must be addressed for responsible implementation. Big data and ML are currently being utilized in defence applications such as intelligence gathering, predictive analytics, cybersecurity, and logistics. They offer benefits like enhanced situational awareness, threat detection, and optimized resource allocation. However, challenges include data quality, integration, computational requirements, privacy, and ethical issues like algorithmic bias. The literature review highlights the growing interest in leveraging big data and ML for defence applications. It identifies key areas of application and discusses the benefits and challenges associated with their integration. The review also emphasizes the need for ethical guidelines, regulatory frameworks, and responsible innovation to ensure the responsible use of these technologies. Recommendations for policymakers, defence organizations, and researchers include developing regulatory frameworks, implementing robust data governance, and fostering interdisciplinary collaboration. Ethical guidelines emphasize data privacy, transparency, accountability, and the responsible use of ML systems. Future research should focus on advanced data integration techniques, emerging ML methods, and the ethical implications of AI in defence. The responsible and ethical adoption of big data and ML in defence is essential to ensure national security while up to ethical and legal standards.