Wi-Fi based activity recognition serves a multitude of applications in fields such as smart homes, health care, and security. The property of the channel state information to provide fine-grained information has been utilized in the above-mentioned fields. However, we wanted to diversify the use of channel state information to even broader areas and that’s why used it to detect the water flow and differentiate between different water patterns. Given the huge scarcity of water in the current world our work tends to serve a practical purpose. The basic idea is to utilize the signal variation caused due to the water flow pattern. The static objects such as furniture, still human causes reflection of signal whereas dynamic or moving objects causes additional propagation paths. These additional propagation paths can be observed by measuring the channel state information amplitude between the two routers. There have been a number of challenges on the way which needed to be figured out before we can get a satisfactory result. Some of them include removing the background noise from the CSI data collected, selecting a classifier which can accurately differentiate between the different patterns of the water flow. We performed various signal processing techniques to reduce the noise and to get a much better representation of the CSI waveform. The Multi-class SVM classifier was modeled and trained to predict the accuracy of different labels collected. Our model achieved 90.35 % accuracy in classifying different labels. Considering this as base work for the detection of water flow pattern, accuracy can be improved later on with some additional functionalities.