<p>Road accidents get significant costs to individuals, society, and infrastructure. To address this issue, we propose a comprehensive framework integrating enhanced machine learning techniques for predicting road accident costs. The problem statement revolves around the necessity for accurate forecasting of accident costs to inform policy decisions, allocate resources efficiently, and improve road safety measures. Our proposed system leverages Recurrent Neural Networks (RNNs) with Attention Mechanisms to capture temporal dependencies and identify crucial features in USA Accidents dataset. The process flow of our framework begins with data preprocessing, including feature extraction and normalization, followed by the construction of the RNN-based predictive model. Attention mechanisms are employed to enhance the model’s capability to focus on relevant information within the input sequences, thereby improving prediction accuracy. The model is trained using historical accident data supplemented with contextual information such as weather conditions, road infrastructure, and demographic factors. Evaluation is conducted through rigorous cross-validation techniques and comparison with baseline models to assess the effectiveness of our approach. The results demonstrate that our proposed framework outperforms traditional methods and achieves superior predictive accuracy in estimating road accident costs. Furthermore, sensitivity analysis is conducted to assess the impact of different factors on cost prediction, providing insights for stakeholders and policymakers. Overall, our framework offers a robust and efficient solution for predicting road accident costs, facilitating proactive measures to mitigate their adverse effects on society and infrastructure.</p>