This survey provides a comprehensive overview of the application of deep learning in mobile and wireless networking. The rapid growth of mobile devices and the increasing demand for mobile data traffic have necessitated the development of more efficient and intelligent network management techniques. Deep learning, with its ability to automatically extract features and handle complex data, offers promising solutions for addressing the challenges in mobile and wireless networking. The paper discusses the integration of deep learning techniques with mobile and wireless networking, highlighting the potential of deep learning in various domains such as network traffic analysis, security, and resource management. It also addresses the challenges and limitations of deep learning in mobile environments, including computational demands, data dependency, and the need for robustness against adversarial attacks. The survey emphasizes the importance of selecting appropriate deep learning models and frameworks for mobile networking applications, while also discussing the necessary hardware and software support for effective deployment. The paper concludes with future research directions and open challenges in the field of deep learning for mobile and wireless networking.This survey provides a comprehensive overview of the application of deep learning in mobile and wireless networking. The rapid growth of mobile devices and the increasing demand for mobile data traffic have necessitated the development of more efficient and intelligent network management techniques. Deep learning, with its ability to automatically extract features and handle complex data, offers promising solutions for addressing the challenges in mobile and wireless networking. The paper discusses the integration of deep learning techniques with mobile and wireless networking, highlighting the potential of deep learning in various domains such as network traffic analysis, security, and resource management. It also addresses the challenges and limitations of deep learning in mobile environments, including computational demands, data dependency, and the need for robustness against adversarial attacks. The survey emphasizes the importance of selecting appropriate deep learning models and frameworks for mobile networking applications, while also discussing the necessary hardware and software support for effective deployment. The paper concludes with future research directions and open challenges in the field of deep learning for mobile and wireless networking.