Robotic manipulation in dynamic environments presents complex challenges, requiring synchronized and adaptive approaches. This study seeks to enhance multi-agent collaboration among robots engaged in cooperative manipulation tasks. By integrating a novel algorithmic framework based on reinforcement learning and swarm intelligence, we aim to improve the coordination efficiency and task execution accuracy of robotic agents. We conducted experiments using a multi-robot setup, wherein each robot agent was equipped with advanced sensory capabilities and computational power to perform real-time data processing and decision making. Our findings indicate a significant increase in task efficiency, with a reduction in collision rates and energy consumption. The results demonstrate the potential of leveraging multi-agent systems in industrial applications where precision and adaptability in dynamic environments are paramount. The study concludes that this approach not only improves operational efficiency but also paves the way for further exploration of intelligent robotic systems capable of autonomous decision-making in unpredictable scenarios.