system. İt helps in the areas like improving stability of grid , energy planning and to support the effective market operation. This paper is an attempt to examine traditional as well as ad-vanced forecasting methods, from the classical statistical approaches to modern data-driven and hybrid techniques. The traditional techniques including time-series analysis as well as the numerical weather prediction (NWP) techniques are quite good but are incapable of capturing the complexity and variation patterns of wind pattern. While the cutting-edge techniques, including the machine learning & deep learning have helped to increase the forecasting accu-racy, hybrid models, have given increasingly promising results as they offer a balance between the high accuracy and computational requirements by merging the traditional and modern approaches used for wind speed and/power forecasting. This study shows the significant value achieved by hybrid approachs, reporting Root Mean Square Error (RMSE) values of 0.1089 m/s for statistical approach, 0.02 m/s for intelligent approaches, and 0.0096 m/s for hybrid ap-proaches. Using a real-world wind dataset, the performance of several widely used forecasting models is evaluated and compared. This study provide an symmetrical analysis of advantages and disadvantages of various forecasting approaches across different time scale & weather condition. It also elaborates persistent challenges, e.g., limited data availability and the re-quirement for better model interpretability as well as real-time adaptability. The review con-cludes that although data-driven and hybrid models currently achieve the best performance, additional research is needed to enhance interpretability and data integration. This research improve reviews on wind forecasting, highlighting latest developments and practical uses. İt also provide helpful guide for researchers and idustry experts to understand present & future opportunities in the field.