Printer forensics is a specialized field within digital and document forensics that focuses on identifying the source printer of a printed document through intrinsic and extrinsic characteristics. As printers play a crucial role in both legitimate and malicious activities ranging from document authentication to the dissemination of forged or anonymous materials, the need for robust forensic techniques has become increasingly important. This paper provides a comprehensive overview of the current landscape in printer forensics, including the classification of methods used for source identification, such as mechanical defect analysis, texture pattern recognition, and embedded code detection. Both traditional image processing techniques and recent advancements leveraging machine learning and deep neural networks are examined. Additionally, we explore the challenges associated with dataset availability, print-scan noise, and cross-model generalization. By surveying existing methodologies and the public limitations of current approaches, we identify emerging trends and propose potential directions for future research in the field.