This review aims to provide a holistic understanding of prognostics and health management (PHM) techniques in wind energy, particularly in the estimation of remaining useful life (RUL) of wind turbine (WT) components. The study begins with an introduction that discusses the principles of PHM and its critical role in the wind energy sector. This is followed by an overview of WT systems and the importance of accurate RUL predictions for specific failure modes. Then, various data sources, methods of feature extraction, and criteria for constructing health indices are explored, along with techniques for threshold determination. Degradation modeling techniques, essential for RUL prediction, are examined through three approaches: physics-based models, data-driven methods (including statistical and artificial intelligence techniques), and hybrid models. The performance of these models is evaluated using specific metrics which have been explored. Next, predictive maintenance strategies, optimized using RUL predictions, are presented to minimize downtime and maintenance costs. The paper concludes by identifying future research directions, emphasizing the need to manage uncertainty, integrate physical knowledge, address variable environmental and operational conditions, overcome data issues, and handle system complexity.