This research explores the use of deep learning, specifically Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to determine the location and classify faults on the 161kV transmission line from Aboadze Thermal Power Station through Takoradi, Tarkwa, and Prestea to New Obuasi in Ghana. The critical nature of this transmission line, which supplies power to one of Ghanas richest gold mining communities, necessitates a reliable and accurate fault detection system. This study aims to utilize deep learning approaches to predict, locate, and classify faults to ensure an uninterrupted power supply. The methodology includes data collection from the transmission line, training LSTM and CNN models, and evaluating their performance. Results indicate high accuracy in fault prediction and classification, facilitating timely and effective maintenance. The study concludes with recommendations for integrating these models into the power grids fault detection systems to enhance reliability and efficiency.This research explores the use of deep learning, specifically Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to determine the location and classify faults on the 161kV transmission line from Aboadze Thermal Power Station through Takoradi, Tarkwa, and Prestea to New Obuasi in Ghana. The critical nature of this transmission line, which supplies power to one of Ghanas richest gold mining communities, necessitates a reliable and accurate fault detection system. This study aims to utilize deep learning approaches to predict, locate, and classify faults to ensure an uninterrupted power supply. The methodology includes data collection from the transmission line, training LSTM and CNN models, and evaluating their performance. Results indicate high accuracy in fault prediction and classification, facilitating timely and effective maintenance. The study concludes with recommendations for integrating these models into the power grids fault detection systems to enhance reliability and efficiency.