This study rolls out a new approach called Twin Delayed Deep Deterministic Policy Gradient-enhanced Adaptive Potential Field (TD3-EAPF). It essentially marries the TD3 algorithm with the artificial potential field method to tackle the tricky problem of unmanned aerial vehicle (UAV) path planning when encountering moving obstacles. The proposed method keeps UAVS from getting stuck, wobbling all over the place, or wasting battery life. To enhance its robustness, some dynamic gain tweaks, curvature limits, and a double-check system are brought to boost the real-time response capability. We put the TD3-EAPF algorithm through its paces in twelve different dynamic scenarios, using obstacles shaped like circles, ellipses, and sine waves. The simulations showed that it’s a dab hand at dodging obstacles, delivering paths that are a good 27.3% better than your standard APF, all while sipping energy. The results highlight the performance of the proposed algorithm, especially in crucial situations like getting communication networks back up and running after a disaster or inspecting tight spaces.