The article discusses the development of real-time UAV recognition systems to enhance military security. It highlights the increasing use of Unmanned Aerial Vehicles (UAVs) in military operations and the need for accurate and real-time identification of UAVs from other objects, such as birds. The study compares advanced machine learning models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines, to determine the most effective ones for UAV recognition. The results show that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize the potential of Neural Networks to improve operational security and resource allocation in military contexts. The research advocates for the integration of Neural Networks into military defense systems to enhance decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology. The article also discusses the importance of real-time processing, minimizing false positives and negatives, and evaluating system robustness in simulated environments. It concludes that machine learning has significant potential in real-time UAV recognition and suggests future research directions to improve UAV detection systems. The study contributes to the field of military surveillance by proposing advanced detection algorithms, real-time data processing techniques, and strategies for minimizing false alarms. The research highlights the effectiveness of machine learning in UAV recognition and emphasizes the need for continued advancements in this area to address the growing threat posed by UAVs in military contexts.The article discusses the development of real-time UAV recognition systems to enhance military security. It highlights the increasing use of Unmanned Aerial Vehicles (UAVs) in military operations and the need for accurate and real-time identification of UAVs from other objects, such as birds. The study compares advanced machine learning models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines, to determine the most effective ones for UAV recognition. The results show that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize the potential of Neural Networks to improve operational security and resource allocation in military contexts. The research advocates for the integration of Neural Networks into military defense systems to enhance decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology. The article also discusses the importance of real-time processing, minimizing false positives and negatives, and evaluating system robustness in simulated environments. It concludes that machine learning has significant potential in real-time UAV recognition and suggests future research directions to improve UAV detection systems. The study contributes to the field of military surveillance by proposing advanced detection algorithms, real-time data processing techniques, and strategies for minimizing false alarms. The research highlights the effectiveness of machine learning in UAV recognition and emphasizes the need for continued advancements in this area to address the growing threat posed by UAVs in military contexts.