车联万物（V2X）通信系统依赖于既包含车辆轨迹数据、又涵盖具备真实保真度的无线网络参数的数据集，以支撑预测与优化模型的构建。当前存在一项极为关键的研究基础设施缺口：公开可用的数据集通常仅覆盖移动性或网络参数二者之一，极少提供将两者统一整合的单一视图。本文提出MINT-V2X，该数据集通过耦合SUMO交通动力学仿真与OMNeT++/Simu5G网络仿真生成。其验证框架由14项标准化测试构成，依据3GPP Release 14（C-V2X）、ETSI标准及香农容量理论设计。最终数据集包含3小时城市交通仿真中1,386辆车辆与15个路侧单元（RSU）产生的987万条同步数据点。我们通过网络指标相关性（CQI-SINR：0.993；SINR-PDR：0.946）验证了严格的算法一致性。最后，我们通过RSU负载预测案例研究展示了该数据集的价值：相比仅依赖网络历史数据的基线方法，引入轨迹数据可显著提升预测性能。该数据集、实验代码及完整的SUMO配置文件均已开源至GitHub仓库，以支持在其他仿真平台上的复现。

Vehicle-to-Everything (V2X) communication systems are based on datasets that not only contain vehicle trajectory data but also wireless network parameters with a realistic level of fidelity, enabling the creation of prediction and optimization models. There is a very critical research infrastructure gap today, and publicly available datasets are likely to be limited to one of the two: mobility or network parameters, and rarely provide a single, integrated view that combines both. This paper introduces MINT-V2X, a comprehensive dataset generated by coupling SUMO traffic dynamics with OMNeT++/Simu5G network simulation. The validation framework is composed of 14 standardized tests based on 3GPP Release 14 (C-V2X), ETSI standards and Shannon capacity theory. The resulting dataset contains 9.87 million synchronized data points from 1,386 vehicles from 15 roadside units (RSUs) during 3 hours of urban traffic simulation. We demonstrate strict algorithmic consistency through network metric correlations (CQI-SINR: 0.993; SINR-PDR: 0.946). Finally, we demonstrate the value of the dataset by conducting an RSU load prediction case study, showing that using trajectory data yields better predictive performance than network-history-only baselines. The dataset, experiments, and complete SUMO configuration files are available in the GitHub repository to facilitate reproduction on alternative simulation stacks.