  Traffic accidents pose a severe global public health issue, leading to 1.19
million fatalities annually, with the greatest impact on individuals aged 5 to
29 years old. This paper addresses the critical need for advanced predictive
methods in road safety by conducting a comprehensive review of recent
advancements in applying machine learning (ML) techniques to traffic accident
analysis and prediction. It examines 191 studies from the last five years,
focusing on predicting accident risk, frequency, severity, duration, as well as
general statistical analysis of accident data. To our knowledge, this study is
the first to provide such a comprehensive review, covering the state-of-the-art
across a wide range of domains related to accident analysis and prediction. The
review highlights the effectiveness of integrating diverse data sources and
advanced ML techniques to improve prediction accuracy and handle the
complexities of traffic data. By mapping the current landscape and identifying
gaps in the literature, this study aims to guide future research towards
significantly reducing traffic-related deaths and injuries by 2030, aligning
with the World Health Organization (WHO) targets.
