To improve early detection and prevention efforts, this study investigates the use of artificial intelligence methods to predict forest fires. Various models of machine learning are developed to forecast the risk and severity of forest fires in certain places by using historical data on climatic elements including temperature, humidity, wind speed, and plant cover. Meaningful from the input data, characteristics are retrieved using feature engineering approaches, and predictive performance is maximised through model selection and hyperparameter optimization. The trained models are assessed using measures including F1-score, recall, accuracy, and precision to determine strategy for predicting forest fires. The results of this study assist in the development of more accurate and reliable prediction models for techniques for managing and mitigating forest fires. This is a serious environmental issue that degrades the environment by endangering the natural resource landscape, upsetting ecosystem stability, raising the possibility of more natural disasters, and reducing resources like water, which contributes to water pollution and global warming. One essential component in managing these occurrences is fire detection. It is expected that forest fire forecasting would mitigate the consequences of subsequent wildfires. Many fire detection algorithms are available, and they all tackle the task of locating flames differently. The present work takes advantage of the satellite photographs procedures to anticipate the area impacted by the fire.