The inverse location of the end-effector for robotic manipulators, particularly those with more than six degrees of freedom, remains a challenging problem due to the highly nonlinear and often redundant nature of the kinematic equations. This paper introduces a hybrid approach that leverages deep learning as an efficient initial guess generator for solving IK problems, followed by an optimization-based refinement using the Adaptive Exploration Artificial Bee Colony algorithm. Deep learning models were trained to map the end-effector’s desired location to approximate joint configurations. While the neural network achieves reasonable accuracy, challenges like generalization to unseen configurations and handling kinematic singularities persist. To address these, the AEABC algorithm refines the network’s predictions, optimizing them to minimize pose error in Cartesian space. Results illustrate that the hybrid approach significantly improves accuracy compared to standalone deep learning or traditional optimization methods.