Biological collectives such as slime molds, fungal networks, and bird flocks exhibit robust self-organization and adaptability emerging from decentralized interactions among simple agents. This work investigates how these organizational principles can be abstracted, formalized, and transferred to swarm robotic systems to achieve scalable coordination, resilience, and fault tolerance. We adopt a data-driven framework that integrates machine learning techniques, including neural networks and reinforcement learning, with physics-based computational mechanics simulations. These simulations generate rich spatiotemporal datasets capturing complex agent–environment interactions, providing physically grounded training data and enabling systematic evaluation under realistic operating conditions. The learned decentralized control policies are implemented and tested within an in-house multiagent simulation platform and subsequently transferred to physical swarm robot prototypes developed in our laboratory, enabling direct validation of sim-to-real performance. Applications include distributed sensing, adaptive exploration, and decentralized task allocation in unstructured environments. Overall, this work demonstrates that combining biologically inspired organization with data-driven learning and mechanics-informed modeling leads to robust and scalable swarm robotic architectures suitable for real-world deployment.