  Artificial Intelligence (AI) has recently shown its capabilities for almost
every field of life. Machine Learning, which is a subset of AI, is a `HOT'
topic for researchers. Machine Learning outperforms other classical forecasting
techniques in almost all-natural applications. It is a crucial part of modern
research. As per this statement, Modern Machine Learning algorithms are hungry
for big data. Due to the small datasets, the researchers may not prefer to use
Machine Learning algorithms. To tackle this issue, the main purpose of this
survey is to illustrate, demonstrate related studies for significance of a
semi-parametric Machine Learning framework called Grey Machine Learning (GML).
This kind of framework is capable of handling large datasets as well as small
datasets for time series forecasting likely outcomes. This survey presents a
comprehensive overview of the existing semi-parametric machine learning
techniques for time series forecasting. In this paper, a primer survey on the
GML framework is provided for researchers. To allow an in-depth understanding
for the readers, a brief description of Machine Learning, as well as various
forms of conventional grey forecasting models are discussed. Moreover, a brief
description on the importance of GML framework is presented.
