This paper presents an Enhanced Adaptive B-Spline Smoothing approach for UAV path planning in complex three-dimensional environments. By leveraging the inherent local control and smoothness properties of cubic B-Splines, the proposed method integrates an adaptive knot selection mechanism—optimized via a genetic algorithm—with curvature-aware control point refinement to generate dynamically feasible and smooth flight paths. Simulation studies in a cluttered 3D airspace show that the proposed technique reduces path length and lowers maximum curvature compared to uniform and chord-length-based B-Spline strategies. Despite a moderate computational overhead, the results demonstrate smoother, more stable flight trajectories that adhere to aerodynamic constraints and ensure safe obstacle avoidance. This approach is particularly valuable for near-real-time missions, where flight stability, rapid re-planning, and energy efficiency are paramount. Results emphasize the potential of the proposed method for improving UAV navigation in various applications—such as urban logistics, infrastructure inspection, and search-and-rescue—by providing better maneuverability, reduced energy consumption, and increased operational safety to the UAV agents.