Wireless networks, such as heterogeneous or large networks, very ultra-dense network, and unmanned aerial vehicle networks, are becoming more autonomous, complicated, and dynamic due to the booming number of mobile devices and sensors. Concurrently, it leads to a fast escalation in energy consumption and the necessity for efficient resource distribution. These networks aim to substantially enhance data speeds, coverage, and the quantity of connected devices, while reducing latency and energy consumption. The issue of distributing wireless network resources is further exacerbated by the restricted energy resources of users’ devices and sensors. This paper offers an extensive review of deep reinforcement learning (DRL) applications in device-to-device communications and networking. Device-to-device technology permits direct connections between nearby devices and may serve as a viable solution for mitigating the increase in mobile traffic. Device-to-device (D2D) communication is an innovative wireless access method enabling proximate user equipment to connect directly, bypassing the base station (BS). Direct D2D lines may enhance cellular bandwidth spectrum and power and energy efficiency, alleviate base station traffic, and expand wireless network coverage by integrating them into the existing cellular infrastructure.