Foley effects are used in multimedia post-production, where the reproduction of everyday sound effects that are added to film, multimedia, or videos enhances its audio quality. In general, Foley effects are created by Foley artists with the help of a variety of props and techniques in a studio setup. These foley effects include various ambient noises such as footsteps, car racing, breaking of glass, thundering, rain, waterfall, etc. Artificial Intelligence and Machine learning techniques help to generate foley effects without a studio setup and evaluate the generated sounds for measurement of enhancement in the existing audio visual effects for specific scenes. This AI ML approach of Foley effect generation is known as AutoFoley. AutoFoley effect generation has attracted the attention of researchers recently as a fully automated version of Foley effects generation with higher performance accuracy has the scope of research according to the literature. This paper provides an overview of the terms foley effect, auto foley, sound synthesis, and sound analysis. Various steps required for sound synthesis and the algorithms available in the literature are also addressed here. Sound synthesis research challenges are also enlisted in this survey. The review lists the existing AI ML methods for AutoFoley sound generation and their average accuracy for specific datasets in the literature.