This paper presents a novel approach to target tracking by leveraging the neurobiological mechanisms of the honey bee brain. We develop a bio-inspired model that replicates the visual processing and navigational strategies of honey bees, aiming to enhance tracking accuracy and efficiency in autonomous systems. The modeling phase involves constructing a computational framework that simulates the honey bee’s sensory inputs and decision-making processes. Through comprehensive analysis, we examine the system’s dynamic behavior and performance across various environmental conditions. Subsequently, we design a control system that incorporates these bio-inspired principles, resulting in a robust and adaptive target tracking solution. Experimental results demonstrate the effectiveness of our approach, highlighting its potential applications in robotics, UAVs, and other autonomous systems. This study contributes to the field by offering a biologically inspired paradigm for improved target tracking and control system design.