Wind energy holds immense promise for sustainable development, yet its integration into power grids remains challenging due to the complex and variable nature of wind. These inherent complexities make accurate wind speed prediction a critical area of research, aimed at enhancing the reliability and efficiency of wind energy systems. Among various techniques applied, data preprocessing has played a crucial role in improving the accuracy of wind speed prediction models. This paper focuses on evaluating data preprocessing algorithms, distinguishing itself by adopting a data-driven approach. By systematically analyzing numerical data from existing scientific literature, this study enables a comparative assessment of diverse data preprocessing methods used for wind speed prediction. The findings offer valuable insights, ultimately guiding practitioners in selecting or reconsidering specific preprocessing techniques based on their impact on the prediction accuracy. This research contributes to advancing predictive reliability, supporting the broader adoption of wind energy within sustainable power grids.