Cooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a Neural Network-Sliding Mode Controller (NN-SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point-to-point modes, the NN-SMC dynamically generates real-time Switching Mode Controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point-to-point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3-DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network-based control, promising enhanced efficiency and stability.