The chapter is related to the safe and energy-efficient motion of autonomous drones and describes the developed novel immune neural network-based machine learning technology for its control. The technology is inspired by two biological systems – immune system and neural networks and their artificial analogs and evolutionary theory. The developed novel mathematical models and algorithms for this technology allow skipping the preliminary supervised training step and adapted for real-time continuous unsupervised self-learning of the drone to recognize the dangerous situation, to prevent the collision by making control decisions autonomously and continuously learning to keep optimal energy consumption during the motion. The chapter includes the study of existing neural network-based solutions for the recognition and prevention of dangerous situations and energy efficiency of drones, describes the developed target function and algorithm for immune neural network machine learning technology and simulation and experimental results proving the efficiency of this technology