Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). The present study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Gujarat State, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Gujarat, India, before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. The maps that result depict the geographical distribution of normalized burn ratio and difference normalized burn ratio and land surface temperature forecasts, providing valuable insights into spatial patterns and trends. The findings of this work show that an automated temporal analysis utilizing Google Earth Engine may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats. The impact of forest fires can be severe, leading to the loss of biodiversity, damage to ecosystems, and threats to human settlements.