Distracted driving-related accidents are a critical global issue, especially as road traffic increases in densely populated areas. To address the challenge of driver distraction, we introduce a novel dataset that supports the development of real-time monitoring and detection systems by capturing authentic driver behaviors. Collected in Ashulia, Dhaka, Bangladesh, in October 2024, this dataset includes images captured under real-world driving conditions within both private vehicles and public buses. The photos were taken using personal mobile phones, ensuring a realistic and diverse set of visual data. This dataset spans a wide range of driving behaviors, including safe driving, turning, texting, talking on the phone, and other potentially risky behaviors, such as drowsy driving. By depicting these behaviors in everyday driving scenarios, the dataset serves as a valuable resource for training and evaluating models designed to detect unsafe driving practices in real-time.The dataset includes high-resolution photos taken inside public buses and personal cars in Ashulia, Dhaka, Bangladesh, under actual driving circumstances. The photographs, which were taken using the cameras on personal cell phones, offer a genuine and varied collection of visual information under normal driving circumstances. The following five behavioral classes comprise the dataset: I. Safe Driving: Images showing a driver who seems to be paying attention to the road, both hands on the wheel, and concentrated or 1 hand on the steering wheel and other on the gear stick. This is the perfect example of driving without distractions. II. Turning: Photographs that show drivers changing direction during turns by moving their heads or full bodies. This behavior is crucial for figuring out how focused the driver is on everyday tasks like rotating the steering wheel. III. Texting Phone: Pictures of drivers using their phones, whether it is to type messages or to interact with the screen. Since texting and driving is one of the main causes of distracted driving, this training is very important for identifying it. IV. Talking Phones: When drivers talk on their phones or hold them up to their ears while driving a vehicle. This category aids in identifying actions connected to phone talks, which are another frequent source of interruptions. V. Others: Contains any actions that go against safe driving practices, like drinking water or anything while driving, sleeping while driving, or talking with someone behind while driving. Relevant photos are included in each session, and they differ in terms of vehicle type and illumination to represent the variety of driving situations found in the real world. Because the images are unprocessed and unannotated, there is freedom in how machine learning