<p>Unmanned aerial vehicles are being deployed in complex, safety-critical, missions that require a high autonomy level, especially in planning and control in hazardous or inaccessible environments. This review provides a comprehensive analysis of two fundamental pillars of dual-layer autonomy, that is, path planning and control techniques applied in autonomous vehicles. First, this review systematically classifies and evaluates planning techniques into global and local methods, including graph-based, sampling-based, potential field, metaheuristic, and artificial intelligence approaches. The second part analyzes control methods, covering classical linear, modern nonlinear, intelligent, and hybrid control strategies. For each technique, we discuss the basic concept, advantages, disadvantages, and challenges. Quantitative comparisons reveal trade-offs between computation time, path optimality, and environmental adaptability, and robustness to disturbances. This analysis demonstrates how hybrid methods mitigate individual limitations to reveal trade-offs between these factors. We introduce unifying frameworks, including a dual-layer autonomy model and a novel taxonomy of hybridization patterns to contextualize methods. Moreover, the paper discusses advancements, challenges, and future trends in aircraft autonomy. Finally, we synthesize a research roadmap focused on the convergence of model-based and data-driven approaches, such as learning-augmented optimal controller and certifiable neuro-symbolic autonomy, addressing persistent challenges in uncertainty, safety, autonomous, and real-world applications.</p>