Leak detection is an important issue to be resolved of pipeline networks integrity and early leak detection and monitoring is highly demanded in the water network systems and energy transport field, since it can minimize environmental impact and economic loss. Nowadays, most leak detection methods require equipment to be used and/or installed which can be costly and often involve a lot of modelling and simulation. The goal of this work is to offer a solution for accurate in-pipe leak position detection by design of swimming robot (passively in-pipe swimming ball) that is equipped with an Inertial measurement Unit (IMU) board and a microphone required for leak localization. The data (Gyros, Accels) measured by IMU are used in calculating the position of the in-pipe swimbot and the deep learning classifier is applied using data recorded by the microphone to identify different acoustic characteristics of leak sounds.