Forest fires are emitting substantial amounts of greenhouse gases and particulate matter into the atmosphere than assumed in state climate targets. It can play an important role in combustible environments, such as shrublands, grasslands, and forests, and contribute to climate change. Thus, forest fire, and climate change is intertwined concepts. As vegetation burns, release the carbon stored within them. This is the main reason why large-scale forest fires release atmospheric carbon dioxide (CO2) and hence, are responsible for increasing the rate of climate change to a great extent. It is extremely significant to measure the contribution of global forest fire and emissions trends of greenhouse gases. In this context, continental-scale carbon emissions assessments were primarily attempted using ground-based datasets for forest ecosystem fires. Considerable research has been published employing remote sensing data from coast to coast. While ground-based data are valuable, they have some restrictions that can be overcome by remote sensing. Ground-based fire data are primarily limited to the total burned area, with their completeness changing yearly with the location. Remote sensing can provide additional spatio-temporal fire information to improve fire emission estimates. In this paper, the factors driving forest fire, with a brief discussion on the triangular relationship between fire, land degradation, and climate change, the role of Remote Sensing and Geographic Information Systems (GIS), machine learning (ML), and a critical overview of state-of-the-art global climate change are presented.