Unmanned aerial vehicles (UAVs), with their exceptional flexibility and mobility, can be rapidly deployed to complex regions where traditional terrestrial infrastructure struggles to provide coverage, such as remote areas and disaster-stricken sites. It effectively addresses the challenges of limited network coverage and high deployment costs in conventional terrestrial mobile edge computing (MEC) networks. As latency-sensitive applications such as the Internet of Things (IoT) and autonomous driving become increasingly prevalent, end-to-end delay has emerged as a vital measure for evaluating quality of service (QoS). The joint optimization of task scheduling strategies, offloading ratios, and UAV flight trajectories directly determines the efficiency of data transmission and computational processing. However, existing studies on delay optimization in UAV-assisted MEC generally overlook the constraint of maximum UAV energy consumption or fail to effectively handle complex decision-making in continuous action spaces. To address these challenges, this paper proposes a novel joint optimization approach based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which collaboratively models task offloading, trajectory planning, and resource allocation under strict energy constraints. This study models the problem using the framework of a Markov Decision Process (MDP). Simulation results demonstrate that the proposed algorithm achieves a reduced average delay compared to baseline algorithms while maintaining stable policy convergence performance. This work provides a viable solution for the low-latency and reliable deployment of UAV-assisted MEC systems in real-world scenarios.