This paper presents the design and analysis of an advanced autopilot system for controlling the lateral and longitudinal dynamics of a custom-built unmanned aerial vehicle (UAV). The system regulates roll, pitch, and yaw angles through a unified control strategy, leveraging recursive least squares (RLS) estimation to model the UAV’s dynamic behavior with high precision. A key innovation lies in applying RLS to extract both lateral and longitudinal transfer functions, offering a level of adaptability rarely seen in comparable studies. To optimize multi-axis control, a digital linear quadratic regulator (DLQR) is developed, enabling simultaneous tuning of all three rotational degrees of freedom. This integrated approach marks a departure from conventional proportional, proportional-integral- derivative or fuzzy logic controllers, which often treat these axes independently. The simulation framework incorporates pseudo-random binary sequence (PRBS) inputs and white Gaussian noise (WGN), ensuring robustness under realistic operating conditions. The UAV platform is grounded in validated aerodynamic data, with stability derivatives obtained from DATCOM and JSBSIM, effectively bridging theoretical modeling with real-world flight dynamics. Simulation results reveal substantial improvements in rise time, settling time, and overshoot across all axes, demonstrating the DLQR controller’s effectiveness in achieving fast, stable, and responsive flight behavior.