This paper presents a deep learning-based system for predicting road accidents using factors such as speed, traffic conditions, and weather. The system aims to enhance road safety by providing real-time insights, route suggestions, and identification of accident-prone areas. It leverages publicly available datasets and external sources to forecast accident likelihood, aiding individuals and authorities in making informed decisions. The study addresses the challenge of predicting road traffic accidents (RTAs) by analyzing various parameters and utilizing advanced technologies like machine learning and data analytics. The paper also reviews existing literature, highlighting studies that used machine learning algorithms like Decision Trees and Random Forests to predict accident likelihood and severity. Additionally, it discusses the RoadGuard Project, which proposes a collaborative platform involving government agencies, law enforcement, and technology companies to improve road safety through machine learning models trained on historical accident data. The methodology section outlines the use of machine learning approaches such as Decision Tree, AdaBoost, KNN, and Naïve Bayes for road accident analysis. The paper emphasizes the importance of government data and open data sources in road accident analysis, while also exploring the potential of social media as a data source. It discusses clustering algorithms, classification algorithms, and natural language processing techniques for analyzing road accidents. The paper concludes that machine learning offers a practical approach to predicting traffic accident severity and suggests using advanced techniques for this purpose. Future enhancements include integrating real-time data and exploring advanced machine learning techniques to improve the system's functionality and usability.This paper presents a deep learning-based system for predicting road accidents using factors such as speed, traffic conditions, and weather. The system aims to enhance road safety by providing real-time insights, route suggestions, and identification of accident-prone areas. It leverages publicly available datasets and external sources to forecast accident likelihood, aiding individuals and authorities in making informed decisions. The study addresses the challenge of predicting road traffic accidents (RTAs) by analyzing various parameters and utilizing advanced technologies like machine learning and data analytics. The paper also reviews existing literature, highlighting studies that used machine learning algorithms like Decision Trees and Random Forests to predict accident likelihood and severity. Additionally, it discusses the RoadGuard Project, which proposes a collaborative platform involving government agencies, law enforcement, and technology companies to improve road safety through machine learning models trained on historical accident data. The methodology section outlines the use of machine learning approaches such as Decision Tree, AdaBoost, KNN, and Naïve Bayes for road accident analysis. The paper emphasizes the importance of government data and open data sources in road accident analysis, while also exploring the potential of social media as a data source. It discusses clustering algorithms, classification algorithms, and natural language processing techniques for analyzing road accidents. The paper concludes that machine learning offers a practical approach to predicting traffic accident severity and suggests using advanced techniques for this purpose. Future enhancements include integrating real-time data and exploring advanced machine learning techniques to improve the system's functionality and usability.