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Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site. [ABSTRACT FROM AUTHOR]