Swarm robotics involves coordinating multiple simple robots to achieve complex tasks through decentralized control and local interactions, mirroring biological swarms. Integrating AI elevates this concept by enabling individual robots to make smarter decisions and the collective to exhibit more sophisticated emergent behaviors. AI, particularly machine learning techniques like reinforcement learning and neural networks, allows the swarm to dynamically learn from its environment and past experiences. This intelligence serves to optimize critical aspects such as task allocation, fault tolerance, and adaptability to unforeseen circumstances. Consequently, AI-powered swarm robots can perform tasks like environmental monitoring, disaster response, and autonomous construction with enhanced efficiency and resilience. The synergy between swarm principles and artificial intelligence promises highly robust and scalable robotic solutions for various challenging real-world problems.