The recent advancements in the field of quadrotor control and UAV applications demonstrate a significant shift towards more adaptive, efficient, and robust solutions. Key developments include the integration of deep reinforcement learning (RL) for real-world deployment, the use of meta-learning for system identification, and the application of physics-informed neural networks for health monitoring. These innovations are enhancing the capabilities of UAVs in tasks ranging from artistic manipulation to energy harvesting from turbulent winds. Notably, RL-based approaches are being refined to bridge the sim-to-real gap, while multi-task RL frameworks are enabling quadrotors to perform a variety of maneuvers without the need for complete retraining. Additionally, the introduction of learnable and adaptive representations for nonlinear dynamics is advancing system identification, and hybrid methods combining neural networks with physics-based models are providing efficient solutions for engine health monitoring. These trends collectively point towards a future where UAVs are not only more versatile but also more autonomous and capable of handling complex, real-world scenarios with greater precision and efficiency.