The efficiency and reliability of real-time incident detection models directly impact the affected corridors’ traffic safety and operational conditions. The recent emergence of cloud-based quantum computing infrastructure and innovations in noisy intermediate-scale quantum devices have revealed a new era of quantum-enhanced algorithms that can be leveraged to improve real-time incident detection accuracy. In this research, a hybrid machine learning model, which includes classical and quantum machine learning models, is developed to identify incidents using the connected vehicle (CV) data. The incident detection performance of the hybrid classical-quantum machine learning model is evaluated against baseline classical ML models. The framework is evaluated using data from a microsimulation tool for different incident scenarios. The results indicate that a hybrid neural network containing a 4-qubit quantum layer outperforms all other baseline models when training data is lacking. We have created three datasets: DS-1 with sufficient training data and DS-2 and DS-3 with insufficient training data. The hybrid model achieves a recall of 98.9%, 98.3%, and 96.6% for DS-1, DS-2, and DS-3, respectively. For DS-2 and DS-3, the average improvement in F2-score (measures the model’s performance to identify incidents correctly) achieved by the hybrid model is 1.9 and 7.8, respectively, compared to the classical models. It shows that with a realistic scenario with limited CVs at certain times on certain roadways, the hybrid classical-quantum ML model performs better than the classical models. With the continuing improvements in quantum computing infrastructure, the quantum ML models could be a promising alternative for CV-related applications when the available data is insufficient.