This paper presents the development of advanced technology for the automated deployment of drone swarms equipped with thermal cameras, aimed at locating missing persons or fugitives. Traditional search methods rely heavily on substantial resources such as ground teams, helicopters, sniffer dogs, and judicial police units. Integrating drones into these efforts enhances the search process by providing realtime thermal imaging without interfering with existing methods. The main technical challenge lies in synchronizing and coordinating drone swarms to effectively cover search areas. This is achieved through sophisticated algorithms, including swarm coordination using the Boids model for maintaining formation and preventing collisions, and real-time optimization strategies like the Traveling Salesman Problem (TSP) to maximize area coverage. Human identification leverages convolutional neural networks (CNNs) trained on diverse thermal datasets to detect and classify human silhouettes with high accuracy, minimizing false positives and negatives. Drones dynamically adjust flight plans using AI-driven energy management and pathfinding algorithms to optimize operational efficiency. For this project, the DJI Mavic 3T Enterprise Thermal was selected for its advanced sensors and machine learning capabilities. Thermal drone swarms offer significant benefits, including rapid area coverage, enhanced resource allocation, and real-time decision-making through live image transmission. This initiative aims to equip law enforcement with cutting-edge tools, improving operational efficiency through automated drone management and real-time thermal and optical data analysis.