This comprehensive review article surveys recent advancements in deep learning applications and methods for autonomous navigation. We provide a detailed overview of state-of-the-art deep learning frameworks for key functions in autonomous navigation, including obstacle detection, scene perception, path planning, and control. We analyze recent research studies to evaluate the implementation and testing of these methods, and provide a critical assessment of their strengths, limitations, and potential areas of growth. We also highlight interdisciplinary work related to this field, and discuss the challenges posed by environmental complexity, uncertainty, obstacles, and dynamic environments. Our review emphasizes the importance of navigation for mobile robots, autonomous cars, unmanned aerial vehicles, and space vehicles, and identifies key trends in recent research. By synthesizing findings from multiple studies, we provide a valuable resource for researchers and practitioners working in this field.