Vehicular edge computing (VEC) is a research paradigm that aims to increase computing resources for internet of vehicles (IoV) applications. With the growth in augmented reality services, 3D gaming, and autonomous driving, scheduling tasks based on deadlines is a difficult task in vehicular networks. Efficient task scheduling is one of the primary features that optimize resource efficacy and performance at the periphery servers. This study presents an adaptive task scheduling method that uses a dynamic time quantum (DTQ) approach to handle VEC systems with changing workloads. The solution not only reduces the task turnaround time but also eliminates context-switching overheads by generating variable times that align with the execution needs and deadlines of incoming tasks. The proposed DTQ-based round-robin scheduling algorithm outperformed both the conventional round-robin and deadline-aware round-robin algorithms with STQ in terms of total context switches, waiting time, and average turnaround time. Thus, dynamic solutions for task management in VEC will play a vital role in enabling efficient and reliable vehicular communication networks.