Fire accidents have severe consequences in terms of life loss, injuries and damages to the property. In hazardous areas, firefighters risk their life to safeguard others. In search of minimizing these risks has contributed to the development of small firefighting robots that can respond quickly. This work aims to develop a state-of-the-art firefighting robot where the robot is designed to help firefighters and save lives. In the paper, the firefighting robot is designed with the combination of hardware devices and deep learning algorithms. The system has different hardware devices like Raspberry Pi, Arduino Uno, Pi camera, 3 flame sensors, servo motor, motor driver, DC motors, water pump etc. The three flame sensor cover almost 180 \(^\circ \) area and each flame sensor can detect fire within 30 cm. The system moves to three different directions, like left, right and front, based on which flame sensor detected the fire. After detecting fire by the flame sensor, the Pi camera captures images and send to the YOLOv8 model for identifying the exact location of the fire in the image. The system calculates the distance and angle of fire from the water pipe using Projectile Motion formulas. Finally, the servo motor moves according to the calculated angle to point the water pipe in the exact position of the fire and water pump activates to give water for putting down the fire. The constructed YOLOv8 model for the system is trained with 6520 online images collected from Roboflow Universe and tested with 371 real time images captured by the Pi camera of the proposed system. The advanced firefighting robot achieves accuracy of 76.4%, F1 Score of 0.73 for the test dataset. The Mean Absolute Error (MAE) value for test dataset of the system is equal to 1.14.