1Mrs. K. KAVYA, 2P. HARSHITHA, 3Y. SRINIJA, 4R. AKHIL 03 05 Road accidents are a critical public safety issueespecially in developing countries like India whereincreasing vehicle density and poor trafficmanagement contribute to high fatality rates Thisproject focuses on the development of an intelligentroad accident analysis and accident severityprediction system using machine learningtechniques The system utilizes historical accidentdatasets to identify patterns and relationshipsamong various factors such as road conditionsweather conditions time of accident driverbehavior vehicle type and traffic density Datapreprocessing techniques including cleaningnormalization and feature selection are applied toensure accurate model performance Multiplemachine learning algorithms such as Decision TreeKNearest Neighbors Nave Bayes and AdaBoostare implemented and evaluated to determine themost effective model for predicting accidentseverity The system classifies accidents intocategories such as slight serious and fatal injuriesenabling better understanding of risk factorsExperimental results indicate that machine learningmodels outperform traditional statistical methods inpredicting accident severity with higher accuracyThe proposed system provides datadriven insightsthat assist traffic authorities policymakers andemergency services in proactive decisionmakingand resource allocation Furthermore the systemcan be enhanced with realtime data integration andvisualization dashboards to improve road safetystrategies Overall this project contributes toreducing accident severity and improvingtransportation safety through intelligent predictionand analysis Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026