Over the past few years, the implementation and operation of renewable energy sources (RESs) in the electricity network has emerged as critical for the development of sustainable energy systems. Looking at another aspect, it is imperative to forecast the RES generation due to the variability and uncertainty of RES. In this study, an extensive comparison is made on the use of machine learning algorithms and deep learning techniques in renewable energy forecasting. The following models are concerned, i.e., support vector regression (SVR), decision trees, random forests, artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, deep belief networks (DBNs), and adaptive neuro-fuzzy inference systems (ANFIS). Further, the study assesses the performance of these models regarding mean absolute error (MAE), accuracy, and root mean square error (RMSE). It entails data cleaning and feature extraction of datasets consisting of meteorological data, and prior energy production history. The architectures of each model are explained, and the training and testing steps are described to produce replicable results. According to the findings, DL models specifically CNNs and RNNs shed off the traditional ML models in terms of higher accuracy and robustness. However, issues like quality of data, interpretability of models, and computational issues are pointed out as the key challenges. The assessment highlights the need for more reliable and explainable approaches for RE forecasting and further directions for future research. The study offers important information for those interested in the improvement of energy strategies and integration of renewables into the grid.