Electricity plays a pivotal role in powering economic growth and it is indispensable in our everyday activities. As a result, the demand for electric power is increasing at a rapid pace. Accurate electricity demand prediction is crucial for ensuring reliable energy supply, optimizing resource allocation, and facilitating infrastructure planning. Machine learning techniques have gained prominence in the field of electricity demand forecasting because of their ability to capture complex patterns and improve predictive accuracy. Sultanate of Oman has witnessed substantial growth in electricity consumption, driven by population expansion and rapid industrialization. As the demand for electricity continues to surge, it becomes imperative to harness advanced forecasting methods to anticipate future electricity requirements accurately. This study examines the efficacy of deep learning, especially Long Short-Term Memory (LSTM) neural networks, in comparison to econometric models like ARIMA and Linear Regression for forecasting electricity demand in Oman. The research methodology involves data preprocessing, feature selection using the Granger Causality test, and model evaluation using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Our findings demonstrate that LSTM models outperform the traditional econometric approaches, demonstrating their exceptional capability to understand the intricate patterns of electricity usage. Specifically, the LSTM model achieved the lowest Mean Squared Error (MSE) of 0.0005, Root Mean Squared Error (RMSE) of 0.0244, and Mean Absolute Error (MAE) of 0.0179, with an R² value of 0.983, indicating a nearly perfect explanation of the variability observed in the testing dataset. An extensive analysis of how weather conditions affect electricity demand reveals that factors such as temperature, wind speed, humidity, and the atmospheric pressure play a crucial role in predicting demand. Notably, rainfall emerges as an insignificant predictor in this context. Consequently, these results endorse the integration of sophisticated deep learning methods into the strategic energy planning and policy-making processes to support Oman's economic development based on pivotal weather-related variables.