The hybrid neuro-fuzzy controller represents a significant methodology employed for the control of mobile robots operating within unstructured environments. This paper introduces an innovative neuro-fuzzy technique aimed at addressing the challenges associated with the autonomous navigation of mobile robots in such settings. A critical aspect of robotics is obstacle avoidance, as the primary objective of an autonomous robot is to navigate to its destination without encountering collisions. The objective is to design a system that guides the robot along an unobstructed path to its destination. This innovative approach replaces conventional fuzzy logic with a neural network at the input stage, followed by adaptive inference and defuzzification processes. By streamlining computations, this technique significantly improves the robot’s reaction speed. The effectiveness of this novel neuro-fuzzy control system is evaluated through comparative analysis with existing methods, emphasizing computational efficiency and overall performance metrics.