The rate of car accidents has been increasing in recent years, resulting in losses in human lives, properties and other financial costs. To address this important issue, an embedded machine learning based system is developed. The system is capable of monitoring road conditions, detecting driving patterns, and identifying aggressive driving behaviors. The system is based on neural networks that are trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system can effectively detect potential risks and help mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of both drivers and vehicles. The process of collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions, sudden starting, sudden stop, and sudden entry. The gathered data is then processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in a 91.9% accuracy, 93.6% precision and 92% recall.