Potholes are cracks on the road’s surface that leave a hole behind it. Reporting potholes to accountable bodies at an early stage can save many lives. Therefore, timely inspection and maintenance of potholes are required for smooth transportation. Traditional pothole detection methods are labor-intensive and time-consuming. This research focuses on such gaps and presents an intelligent detecting system that uses a smartphone camera, sensors, and gyroscope for real-time detection of potholes. The proposed model covers two essential functions: i). automated identification of potholes, and ii). notifying users to escape probable accidents. The “Single Shot Multi-Box Detector (SSD)” technique trains the pothole image datasets. For developing the dataset, pothole images are taken and labeled with TensorFlow object detection API. The method achieved 90% accuracy in detecting potholes in used image datasets. Study outcomes can help stakeholders get road profiling information and alerts about potholes for smooth road transportation.