Dissolved Gas Analysis has been one of the most extensively used tools for health monitoring of power transformers. Since its introduction, researchers have been working on to increase its ability to predict the incipient faults more accurately. DGA methods have been shifting from off-line to online monitoring, and interpretation techniques from key gas and ratio methods to graphical methods and more advanced methods. But presently, the DGA data is used to interpret more detailed information about the faults like severity of the fault or involvement of solid insulation. In this paper, an effort has been made to cover some of such emerging techniques in the field of DGA along with issues like stray gassing problem, scheduling DGA inspection etc. With the development of alternate insulating liquids, DGA of such liquids is gaining importance and have been proactively covered in this paper. Further, the extensive implementation of AI techniques for more flexible interpretation of DGA data is also evaluated through this paper.