This paper proposes an efficient Nonlinear Model Predictive Control (NMPC) framework for precise trajectory tracking of quadrotor Unmanned Aerial Vehicles (UAVs). The quadrotor’s nonlinear dynamics are modeled using the Euler-Lagrange formulation, capturing six degrees of freedom while considering system constraints. The NMPC optimizes performance via a quadratic cost function over a finite prediction horizon, integrating advanced trajectory generation and real-time computation capabilities. Simulation results validate the proposed controller’s ability to track accurately across challenging scenarios, maintain stability under disturbances, and ensure computational efficiency for real-time applications. This work provides a practical and robust solution to key challenges in autonomous UAV control.