Kundapura, S.Vishnu Vardhan, M.Apoorva, K.V.2026-02-0620232023 IEEE 2nd International Conference on Data, Decision and Systems, ICDDS 2023, 2023, Vol., , p. -https://doi.org/10.1109/ICDDS59137.2023.10434817https://idr.nitk.ac.in/handle/123456789/29297Wildfires threaten ecosystems, human lives, and infrastructure, necessitating effective detection and prediction methods. In this study, an in-depth analysis of wildfire detection and forecasting is carried out over the Nallamala hills, which stretch across the states of Telangana and Andhra Pradesh. Our approach comprises three significant steps: Active fire analysis, pre-fire analysis, and post-fire analysis. Pre-fire maps were created using the Normalised Difference Vegetation Index (NDVI) during the pre-fire analysis, which involved time series analysis of significant components. For active fire analysis, the first dataset is created by using satellite imagery and its derived products. A dataset is used to train the five different machine-learning models for prediction. Among these models, the Random Forest classifier outperformed the remaining four models (Support vector Classifier, Gradient Boosting Classifier, Logistic Regression, and K-means algorithms) in accurately detecting and predicting active fires. This step enabled real-Time monitoring and prioritisation of firefighting efforts. The burnt area calculation uses the Normalised Burn Ratio (NBR) in the post-fire analysis. The analysis implemented post-fire rehabilitation and restoration efforts, giving essential information on the scope and severity of fire damage. The comprehensive study of all wildfires will provide a detailed picture of what occurred in the past (Timeseries), present (Prediction models), and future (Pre-fire maps), allowing people and government agencies to take precautions against future wildfires. © 2023 IEEE.Andhra PradeshGoogle Earth EngineMachine LearningNallamala HillsNormalised Difference Vegetation Index (NDVI)Support Vector Classification (SVC)TelanganaWildfires