Environmental systems, human lives and property are all at risk from wildfires. Precise forecasts of impending fire ignitions and spread patterns are required for effective wildfire prevention. This research assesses deep learning architectures to create the next generation of wildfire prediction and simulation capabilities. Our sophisticated deep learning approach is trained on a combined dataset of satellite imagery for vegetation health and land cover mapping information and fire hotspot data and terrain data such as elevation and slope direction and aspect and weather data like temperature and humidity and wind speed and precipitation data and historical fire data. The study uses Long Short-Term Memory (LSTM) networks inside recurrent neural networks (RNNs) to capture temporal dependencies of weather patterns and convolutional neural networks (CNNs) to capture spatial patterns in satellite imagery and topographic data. Our research studies CNN-RNN hybrid architectures that combine spatial data features and temporal knowledge for better predictive performance. Availability of fuel in conjunction with topography and weather conditions allows computational models to assess ignition probabilities and predict fire spread rates. Proposed deep learning models are tested for their performance which includes Area under the ROC curve metrics, and accuracy vs. precision and recall, and additional metrics. The study looks at the potential of deep learning forecasting, and also of integrating this with physical fire spread models to produce a robust wildfire simulation platform. A holistic approach provides critical real-time fire management information necessary for resource allocation and wildfire risk assessment activities. Studies show that deep learning can enhance wildfire simulation modeling that achieves better wildfire management results.This research employs Deep learning technology combined with Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) systems to analyze Wildfire prediction and simulation using Satellite imagery Meteorological data and Topographic data for creation of Early warning systems and Wildfire management through Hybrid models and Explainable AI.