Non-terrestrial networks (NTNs), incorporating unmanned aerial vehicles (UAVs) and opticalbase stations (OBSs), are emerging as a key architectural component for the Sixth Generation (6G) wirelessnetworks, particularly in supporting a wide range of vehicular services in the Internet of Vehicles (IoV). Inparallel, free-space optical (FSO) and sub-terahertz (sub-THz) communications are expected to play a centralrole in 6G due to their abundant spectrum resources. However, the deployment of UAV-assisted vehicularservices faces significant challenges, as highly dynamic vehicle mobility and time-varying atmosphericturbulence introduce severe link fluctuations and reliability issues. A hybrid FSO/sub-THz communicationlink is a potential solution to this problem by enhancing link reliability and service continuity, but it neces-sitates an intelligent and adaptive switching mechanism. To address this, we propose a deep reinforcementlearning (DRL) algorithm that integrates both hard-switching and soft-switching mechanisms to guaranteethe UAV-supported quality-of-service (QoS) through real-time trajectory and link optimization. The hybridFSO/sub-THz link is modeled by considering atmospheric attenuation, fading, pointing errors, blockage bybuildings, and weather conditions. Simulation results indicate that the trained UAV can effectively learn fromthe dynamic environment and maintain robust performance using the proposed DRL-based approach