Electric power systems have rapidly grown for the past fifty years As a result, the number of cables in operation and their total length had largely increased. Underground cables have been widely implemented due to reliability and environmental concerns. To improve the reliability of a distribution system, accurate identification of a faulted segment is required in order to reduce the interruption time during fault. Speedy and precise fault location plays an important role in accelerating system restoration, reducing outage time, reducing great financial loss and significantly improving system reliability Therefore, this study presents fault identification, classification and fault location Estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.