Improving the efficiency of drone propulsion is crucial in extending flight times and cutting down on energy losses during dynamic maneuvers. Traditional Brushless DC (BLDC) motor controllers depend on mathematical models and reactive adjustments, which often struggle to keep up with sudden changes in load, wind disturbances, and fluctuations in battery voltage. To tackle these challenges, this study presents an innovative control strategy that merges Model-Free Adaptive Control (MFAC) with a bio-inspired hybrid optimization technique inspired by Eel Foraging and Gooseneck Barnacle behaviors. Unlike model-based methods, MFAC continuously fine-tunes its control actions using real-time sensor data, eliminating the need for motor parameters and making it robust against unpredictable operating conditions. The hybrid optimization algorithm enhances this adaptability by swiftly identifying the most energy-efficient control inputs that ensure necessary thrust while minimizing switching losses, torque ripple, and power consumption. Experimental tests with a 1000 KV BLDC motor show significant improvements in torque–speed stability, rapid convergence in controller tuning, and a marked reduction in power demand during critical drone flight modes like hovering and ascending. These findings suggest that combining MFAC with bio-inspired optimization paves the way for developing high-efficiency UAV propulsion systems that can achieve longer endurance, reliable real-time control, and less reliance on complex motor models.