The following text is a survey of the literature on different edge detection techniques developed throughout the years, specifically for gray images. The study covers a range of approaches from the traditional, such as Sobel, Prewitt, and Roberts, in terms of background mathematics and performance characteristics. It also discusses, in some detail, the gradient-based algorithms, pinpoints contributions of the various operators in the process of improvement of edge recognition, and the sources of better edge detection on gray-level images. The basic techniques of region-based methods are looked into, and examples are shown in cases where they work better. The survey also goes on to cover the opportunities facing integration and problems with machine learning, specifically deep learning architectures, in the edge detection of grayscale photos. The applications, benchmark datasets, measures for the evaluation, and traditional methods presented herein assure the beginning of further development and research papers in this domain, which is crucial in providing readers with deep insight into the current state of grayscale edge detection.