The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the inter-connectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environ-mental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause events such as mudslides and landslides. The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc., can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire or drought case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a "common-denominator-variable" in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). Insights from EIKG facilitate data analysis using both classical and Quantum Machine Learning (QML) techniques. The EIKG organizes heterogeneous datasets and integrates relationships to address extreme weather events. This study incorporates various datasets, including mobility data, socioeconomic data from the US Census Bureau, climate data from NASA, and critical infrastructure data.