Facial image preprocessing plays a vital role in the performance of facial recognition systems and related applications, such as security, healthcare, and human-computer interaction. The quality of facial images is often compromised due to various factors, including lighting conditions, pose variations, occlusions, and noise. Therefore, efficient preprocessing techniques are essential for enhancing the clarity and usability of facial images in these systems. This review presents an overview of recent advancements in facial image preprocessing techniques, focusing on methods aimed at improving image quality, reducing noise, normalizing lighting, and handling occlusions. A detailed analysis of various preprocessing strategies—such as image enhancement, normalization, and alignment—has been provided, along with their advantages and challenges. The review highlights the importance of integrating deep learning methods with traditional image processing techniques to improve the overall performance of facial recognition systems. Furthermore, future trends and research directions in facial image preprocessing are discussed.