Road traffic accidents remain a major global challenge, contributing to significant human and economic losses each year. In Portugal, the analysis and prevention of severe accidents are critical for optimizing the allocation of law enforcement resources and improving emergency response strategies. This study aims to develop and evaluate predictive models for accident severity using real-world data collected by the Portuguese Guarda Nacional Republicana (GNR) between 2019 and 2023. Four algorithms, Random Forest, XGBoost, Multilayer Perceptron (MLP), and Deep Neural Networks (DNN), were implemented to capture both linear and non-linear relationships within the dataset. To address the natural class imbalance, class weighting, Synthetic Minority Oversampling Technique (SMOTE), and Random Undersampling were applied. The models were assessed using Recall, F1-score, and G-Mean, with particular emphasis on detecting severe accidents. Results showed that DNNs achieved the best balance between sensitivity and overall performance, especially under SMOTE and class weighting conditions. The findings highlight the potential of classical machine learning and deep learning models to support proactive road safety management and inform resource allocation decisions in high-risk scenarios.This research contributes to sustainability by enabling data-driven road safety management, which reduces human and economic losses associated with traffic accidents and supports more efficient allocation of public resources. By improving the prediction of severe accidents, the study reinforces sustainable development goals related to safe mobility, resilient infrastructure, and effective disaster prevention and response policies.