Road traffic crashes (RTC) have become a significant cause of fatalities worldwide. The number of fatalities resulting from accidents is increasing rapidly each day. Therefore, it is crucial to develop early prediction methods that can assist drivers and riders in understanding accident statistics specific to their region. This includes considering factors such as speed limits, adherence to traffic signs, traffic lights, pedestrian crossings, right of way, weather conditions, negligence, fatigue, and the impact of excessive speed on RTC occurrences. In this paper, a stacking method for enhancing the road traffic accident severity classification using machine learning models is presented which consists of several interesting points. Firstly, it offers a promising approach to tackle the challenges associated with accurately classifying accident severity, including imbalanced datasets and high-dimensional features. By combining the predictions of multiple base models, the stacking method creates a meta-model that improves classification performance. This stacking approach enables the exploitation of diverse model strengths, capturing different aspects of the data and enhancing the overall predictive power. Additionally, the selection of appropriate base models plays a crucial role in the success of the stacking method. The participating models should possess complementary strengths and provide robust predictions. Moreover, effective feature engineering and selection techniques can further enhance the performance of the stacking method. It has been found through experimentation and simulation that suggested stacking method has achieved significantly higher performance compared to other related works.