<p>Vehicular communication systems are at the forefront of intelligent transportation networks, enabling advancements in safety, efficiency, and autonomous driving. In the rapidly evolving field of vehicular communication, datasets play a pivotal role in the development and validation of communication protocols. However, the availability and quality of datasets have become crucial in driving innovation and validating new technologies. This paper presents an exhaustive review of the existing datasets used in vehicular communication systems, categorizing them based on their application domains, data types, and technical characteristics. By systematically analyzing these datasets, we identify key trends, gaps, and challenges that impact the development and deployment of vehicular communication technologies. This review article highlights the diversity of datasets in terms of data types, collection sources, and intended applications, offering insights into their suitability for various research objectives. Furthermore, the limitations of current datasets, such as scalability, data quality, and representativeness are discussed which may hinder progress in the vehicular communication field. Based on the challenges findings in the current vehicular datasets, a set of recommendations are proposed for future dataset development, emphasizing the need for standardized data formats, increased coverage of real-world scenarios, and the integration of multi-modal data sources. This review article aims to serve as a valuable resource for researchers, guiding the selection and creation of datasets that can effectively support the next generation of intelligent vehicular communication systems.</p>