This paper presents a deep learning-based system for predicting road accidents, aiming to enhance road safety and reduce fatalities. The system leverages various factors such as speed, traffic conditions, and weather to forecast accident likelihood. By using publicly available datasets and external data sources, the model aims to provide accurate predictions and route suggestions, identifying accident-prone areas. The paper reviews existing research on road accident data analysis, including studies by Dr. Priya Sharma, Dr. Rahul Gupta, Dr. Ankit Patel, and Dr. Neha Singh, which used advanced machine learning algorithms to predict accident likelihood and severity. The methodology section details the use of Decision Tree, AdaBoost, KNN, and Naïve Bayes algorithms for data analysis. The paper emphasizes the importance of government and open data sources, as well as social media, in providing comprehensive insights into road accidents. Future enhancements include integrating real-time data and advanced machine learning techniques to improve the system's accuracy and predictive capabilities. The conclusion highlights the significance of accurate predictions in reducing road accidents and the potential of machine learning in this domain.This paper presents a deep learning-based system for predicting road accidents, aiming to enhance road safety and reduce fatalities. The system leverages various factors such as speed, traffic conditions, and weather to forecast accident likelihood. By using publicly available datasets and external data sources, the model aims to provide accurate predictions and route suggestions, identifying accident-prone areas. The paper reviews existing research on road accident data analysis, including studies by Dr. Priya Sharma, Dr. Rahul Gupta, Dr. Ankit Patel, and Dr. Neha Singh, which used advanced machine learning algorithms to predict accident likelihood and severity. The methodology section details the use of Decision Tree, AdaBoost, KNN, and Naïve Bayes algorithms for data analysis. The paper emphasizes the importance of government and open data sources, as well as social media, in providing comprehensive insights into road accidents. Future enhancements include integrating real-time data and advanced machine learning techniques to improve the system's accuracy and predictive capabilities. The conclusion highlights the significance of accurate predictions in reducing road accidents and the potential of machine learning in this domain.