Big Data's intrinsic size, speed, and complexity are too much for conventional forecasting methods to manage which is caused by the scale and lack of organization in large data sets. Traditional methods are therefore rarely selected for dealing with Big Data. They also lack models that can forecast big data. An example is the abundance of earthquake data, but the absence of a trustworthy model that can reliably forecast earthquakes. TBig data dimensions are primarily characterized by three concepts: volume, velocity, and variety. As contrast to time series, static data has been the primary target of data mining approaches in big data. Although identifying the absence of theory to support big data is an additional worry, some current challenges are connected to hypotheses, testing, and models used for forecasting. One of the biggest issues is finding people with the abilities needed to handle the problem of predicting with big data and their availability. Wind energy, a clean and sustainable energy source, is produced by wind turbines. The movement of water via streams and other channels is known as streamflow or channel runoff, and it is a crucial part of the water cycle. In situations where there are no physical barriers preventing surface flow, runoff occurs when the amount of rain falls exceeds the soil's capacity for infiltration. The review article examines and analyzes the AI-based forecasting techniques used to forecast diverse natural resources such as wind, stream flow and rainfall runoff.