Highlights: What are the main findings? A dual-objective MPC framework is developed for fixed-wing UAVs, simultaneously addressing longitudinal tracking and connectivity-aware trajectory optimization. The proposed MPC outperforms the benchmark LQR in both disturbance rejection and constraint handling under realistic flight conditions. What is the implication of the main finding? The framework enables UAVs to balance flight-time efficiency with reliable cellular connectivity, supporting communication-critical missions. The approach provides a foundation for real-time predictive control integration in 5G-assisted UAV systems. This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A multi-input–multi-output model derived from NASA’s Generic Transport Model (T-2) was used and linearized for controller design. We compared the MPC controller with a Linear Quadratic Regulator (LQR) in MATLAB simulations. The results showed that MPC reached the reference values faster, with less overshoot and phase error, particularly under sinusoidal reference inputs. These differences became even more evident when the UAV had to fly in windy conditions. Trajectory optimization was carried out using the CasADi framework, which allowed us to evaluate paths that balance two competing requirements: reaching the target quickly and maintaining cellular connectivity. We observed that changing the weights of the cost function had a strong influence on the trade-off between direct flight and reliable communication, especially when multiple base stations and no-fly zones were included. Although the study was limited to simulations at constant altitude, the results suggest that MPC can serve as a practical tool for UAV missions that demand both accurate flight control and robust connectivity. Future work will extend the framework to more complete models and experimental validation.